Вакансии

Location: Home based - Worldwide Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is very widely used in breakthrough enterprise initiatives such as public cloud, data science, AI, engineering innovation, and IoT. Our customers include the world's leading public cloud and silicon providers, and industry leaders in many sectors. The company is a pioneer of global distributed collaboration, with 1200+ colleagues in 75+ countries and very few office-based roles. Teams meet two to four times yearly in person, in interesting locations around the world, to align on strategy and execution. The company is founder-led, profitable, and growing. We are hiring an MLOps Field Engineer to help global companies embrace AI/ML in their business, using the latest open source capabilities on public and private cloud infrastructure, Linux and Kubernetes. Our team applies expert insights to real-world customer problems, enabling the enterprise adoption of Ubuntu, Kubeflow, MLFlow, Feast, DVC and related analytics, machine learning and data technologies. We are working to create the world's best open source data platform, covering traditional SQL databases and today's NoSQL data stores, as well as the machinery which turns data into insights and executable models. The people who love this role are MLOps engineers who enjoy customer conversations and solving customer problems during the presales cycle. They are solutions architects who like to solve customer problems through architecture, presentations and training. This role is highly focused on designing ML architectures for external customers. It is not a software development role. This role is particularly suited to candidates with a technical background who are business minded and driven by commercial success. This role is on our global Field Engineering team and will work closely with enterprise sales leads. We are specifically looking for people interested in solving the most difficult problems in modern data architectures. Training LLMs on multiple Kubernetes clusters deployed on a hybrid cloud infrastructure with GPU sharing across multiple teams? Processing 10M events in real time for financial transactions? Object detection on 10k parallel 4K video streams? These are the problems we solve day to day. Location : Most of our colleagues work from home. We are growing teams in EMEA, Americas and APAC time zones, so can accommodate candidates from almost any country. What your day will look like The global Field Engineering team members are Linux and cloud solutions architects for our customers, designing private and public cloud solutions fitting their workload needs. They are the cloud consultants who work hands-on with the technologies by deploying, testing and handing over the solution to our support or managed services team at the end of a project. They are also software engineers who use Python to develop Kubernetes operators and Linux open source infrastructure-as-code. Work across the entire Linux stack, from kernel, networking, storage, to applications, Architect cloud infrastructure solutions like Kubernetes, Kubeflow, OpenStack and Spark, Deliver solutions either on-premise or in public cloud (AWS, Azure, Google Cloud), Collect customer business requirements and advise them on Ubuntu and relevant open source applications, Grow a healthy, collaborative engineering culture in line with the company values, Deliver presentations and demonstrations of Ubuntu Pro and AI/ML capabilities to prospective and current clients, Liaise with product teams to give them feedback on requirements to influence roadmap, Work collaboratively with your sales team to reach our common targets, Global travel up to 30% of time for internal, external events and customer meetings. What we are looking for in you Exceptional academic track record from both high school and university, Undergraduate degree in a technical subject or a compelling narrative about your alternative chosen path, Experience in data engineering, MLOps, or big data solutions deployment, Experience with a relevant programming language, like Python, R, or Rust, Confidence to respectfully speak up, exchange feedback, and share ideas without hesitation, Track record of going above-and-beyond expectations to achieve outstanding results, Demonstrated personal interest in continuous learning and development, Practical knowledge of Linux, virtualization, containers and networking, Business-minded technology thinker and problem solver, Knowledge of cloud computing concepts & leaders, such as Kubernetes, AWS, Azure, GCP, Interest in large-scale enterprise open source - private clouds, machine learning and AI, data and analytics, Intermediate level Python programming skills, Passion for technology evidenced by personal projects and initiatives, The work ethic and confidence to shine alongside motivated colleagues, Professional written and spoken English with excellent presentation skills, Experience with Linux (Debian or Ubuntu preferred), Excellent interpersonal skills, curiosity, flexibility, and accountability, A dynamic person who loves to jump in new projects and interact with people, Appreciative of diversity, polite and effective in a multi-cultural, multi-national organisation, Thoughtfulness and self-motivation, Result-oriented, with a personal drive to follow up and meet commitments, Ability to travel internationally, for company events up to two weeks long, and customer or industry meetings. What you’ll learn Architect and deploy AI/ML infrastructures, data processing pipelines and multi-cluster distributed training, Wide range of open source applications and skills, Work directly with customers in a range of different businesses, Real-life and hands-on exposure to a wide range of emerging technologies and tools. What we offer colleagues We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognize outstanding performance. In addition to base pay, we offer a performance-driven annual bonus or commission. We provide all team members with additional benefits which reflect our values and ideals. We balance our programs to meet local needs and ensure fairness globally. Distributed work environment with twice-yearly team sprints in person Personal learning and development budget of USD 2,000 per year Annual compensation review Recognition rewards 40 days annual leave per annum, including public holidays and our All Company holiday periods Maternity and paternity leave Team Member Assistance Program & Wellness Platform Opportunity to travel to new locations to meet colleagues Priority Pass and travel upgrades for long-haul company events About Canonical Canonical is a pioneering tech firm at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open-source projects and the platform for AI, IoT, and the cloud, we are changing the world of software. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence; in order to succeed, we need to be the best at what we do. Most colleagues at Canonical have worked from home since our inception in 2004.​ Working here is a step into the future and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical is an equal opportunity employer We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration. #LI-remote

Location: Home based - Worldwide The role of an Engineering Manager at Canonical As an Engineering Manager at Canonical, you must be technically strong, but your main responsibility is to run an effective team and develop the colleagues you manage. You will develop and review code as a leader, while at the same time staying aware of that the best way to improve the product is to ensure that the whole team is focused, productive and unblocked. You are expected to help them grow as engineers, do meaningful work, do it outstandingly well, find professional and personal satisfaction, and work well with colleagues and the community. You will also be expected to be a positive influence on culture, facilitate technical delivery, and regularly reflect with your team on strategy and execution. You will collaborate closely with other Engineering Managers, product managers, and architects, producing an engineering roadmap with ambitious and achievable goals. We expect Engineering Managers to be fluent in the programming language, architecture, and components that their team uses, in this case popular open-source machine learning tools like Kubeflow, MLFlow, and Feast. Code reviews and architectural leadership are part of the job. The commitment to healthy engineering practices, documentation, quality and performance optimisation is as important, as is the requirement for fair and clear management, and the obligation to ensure a high-performing team. Location: This is a Globally remote role. What your day will look like Manage a distributed team of engineers and its MLOps/Analytics portfolio Organize and lead the team’s processes in order to help it achieve its objectives Conduct one-on-one meetings with team members Identify and measure team health indicators Interact with a vibrant community Review code produced by other engineers Attend conferences to represent Canonical and its MLOps solutions Mentor and grow your direct reports, helping them achieve their professional goals Work from home with global travel for 2 to 4 weeks per year for internal and external events What we are looking for in you A proven track record of professional experience of software delivery Professional python development experience, preferably with a track record in open source A proven understanding of the machine learning space, its challenges and opportunities to improve Experience designing and implementing MLOps solutions An exceptional academic track record from both high school and preferably university Willingness to travel up to 4 times a year for internal events Additional skills that you might also bring The following skills may be helpful to you in the role, but we don't expect everyone to bring all of them. Hands-on experience with machine learning libraries, or tools. Proven track record of building highly automated machine learning solutions for the cloud. Experience with building machine learning models Experience with container technologies (Docker, LXD, Kubernetes, etc.) Experience with public clouds (AWS, Azure, Google Cloud) Experience in the Linux and open-source software world Working knowledge of cloud computing Passionate about software quality and testing Experience working on a distributed team on an open source project -- even if that is community open source contributions. Demonstrated track record of Open Source contributions What we offer you We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognise outstanding performance. In addition to base pay, we offer a performance-driven annual bonus. We provide all team members with additional benefits, which reflect our values and ideals. We balance our programs to meet local needs and ensure fairness globally. Distributed work environment with twice-yearly team sprints in person - we’ve been working remotely since 2004! Personal learning and development budget of USD 2,000 per year Annual compensation review Recognition rewards Annual holiday leave Maternity and paternity leave Employee Assistance Programme Opportunity to travel to new locations to meet colleagues from your team and others Priority Pass for travel and travel upgrades for long haul company events About Canonical Canonical is a pioneering tech firm that is at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, we are changing the world on a daily basis. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do. Canonical has been a remote-first company since its inception in 2004.​ Work at Canonical is a step into the future, and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical provides a unique window into the world of 21st-century digital business. Canonical is an equal opportunity employer We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration. #LI-remote

Location: Москва Workplace: гибкий Группа разработки HPC-инфраструктуры занимается созданием суперкомпьютера, на котором обучается вся компания. Мы строим HPC-сегменты с использованием технологий InfiniBand и RoCE, а поверх них — ML-платформу на базе Kubernetes. Задачи Разработка и развитие компонентов ML-платформы и инфраструктурных сервисов Автоматизация процессов запуска, сопровождения и мониторинга ML/HPC workloads Повышение надёжности, производительности и масштабируемости платформы Развитие CI/CD и внутренних инструментов для эксплуатации платформы Взаимодействие с командами разработки и ML-инженерами по вопросам платформы и инфраструктуры Требования Опыт разработки инфраструктурных и platform-компонентов на Go Углубленные знания Linux, включая понимание работы ОС, сетевого стека и механизмов контейнеризации Хорошее понимание сетей, сетевых протоколов и диагностики проблем связности Знание архитектуры и принципов работы Kubernetes Навыки troubleshooting на всех уровнях инфраструктуры: хост, контейнеры, runtime, сеть Опыт автоматизации инфраструктурных процессов и разработки внутренних инструментов Понимание принципов надёжности, масштабируемости и observability production-систем Будет плюсом Опыт работы с ML / MLOps / HPC workloads Опыт разработки на Python Опыт работы с CI/CD, monitoring и platform tooling

Location: Москва Workplace: Комбинированный Команда Antifraud в VK Рекламе отвечает за устойчивость и качество рекламной экосистемы. Мы разрабатываем системы, которые в реальном времени выявляют фрод, аномалии и недобросовестное поведение на всех этапах рекламного цикла. Ищем backend-инженера для развития платформы real-time антифрода. Предстоит строить высоконагруженные сервисы, обрабатывающие сотни тысяч запросов в секунду, с жёсткими требованиями ко времени ответа и доступности. В основе задач — обработка рекламных событий, вычисление поведенческих сигналов и применение правил и ML-моделей для принятия решений. Задачи Проектировать и развивать backend-сервисы антифрод-платформы: от приёма событий до принятия решений в реальном времени Обеспечивать высокую пропускную способность и низкую задержку: находить узкие места, проводить нагрузочное тестирование, оптимизировать использование CPU, памяти и сетевых ресурсов Разрабатывать механизмы вычисления и хранения оперативных признаков, счётчиков и агрегатов на больших потоках событий Интегрировать ML-модели и антифрод-правила в обработку рекламных запросов, обеспечивать управляемое обновление и версионирование логики Проектировать отказоустойчивую обработку данных: учитывать повторные и запоздавшие события, перегрузки и недоступность зависимостей Развивать наблюдаемость сервисов: метрики, логи, трассировки и алерты; участвовать в разборе инцидентов Совместно с ML-инженерами и аналитиками внедрять новые механизмы выявления фрода и оценивать их влияние на рекламную систему Требования Уверенное владение Go (Golang) и Python, опыт создания и эксплуатации высоконагруженных сервисов Опыт самостоятельного проектирования сервисов и API, понимание компромиссов между производительностью, надёжностью и сложностью сопровождения Хорошее понимание конкурентной обработки, многопоточности, сетевого взаимодействия и управления ресурсами Знание принципов распределённых систем: партиционирование, репликация, согласованность данных, идемпотентность и гарантии доставки событий Опыт работы с базами данных и кэшами, умение выбирать хранилище под профиль нагрузки и требования к задержкам Практический опыт работы с Kafka: проектирование топиков и партиций, настройка producers и consumers, понимание consumer groups и гарантий доставки событий Опыт работы с Redis для кэширования, хранения оперативных данных и счётчиков; понимание TTL, политик вытеснения и атомарных операций Практический опыт профилирования, нагрузочного тестирования и диагностики проблем в продакшене Опыт работы с Linux, Docker, Git, автоматизированным тестированием и CI/CD Будет плюсом Опыт разработки систем с нагрузкой в сотни тысяч RPS и строгими требованиями к p95/p99 задержкам Опыт разработки на C++ Опыт работы с YTsaurus для хранения и обработки больших объёмов данных Практика эксплуатации сервисов в Kubernetes Опыт интеграции ML-инференса и работы с MLOps-инфраструктурой Опыт разработки антифрод-систем, рекламных платформ, RTB-сервисов или систем принятия решений в реальном времени

Location: Москва Workplace: Гибридный О компании МТС — это цифровая экосистема. Мы создаём и развиваем сервисы в сфере мобильной связи, больших данных, искусственного интеллекта, облачного хранения, медиа и финансов — все они делают жизнь людей проще и интереснее. Обязанности Разрабатывать и актуализировать дорожную карту автоматизации RAN-домена: оценить текущей уровень зрелости по методологии TM Forum ANLET, определить целевые уровни L3–L4 для процессов планирования, оптимизации и развития RAN; Формировать каталог сценариев автоматизации (High-Value Scenarios, HVS): выявлять приоритетные use-cases с максимальным ROI — self-planning, self-optimizing, self-healing, energy saving, capacity management; Внедрять AI/ML-моделей для автоматизации RAN: прогнозировать трафик и качество, автоматическую тюнинг-оптимизацию параметров, детекция аномалий, root-cause analysis, drive-test automation и т.д.; Проектировать и внедрять closed-loop сценариев (intent-based automation) на стыке OSS, EMS/NMS и RAN-инструментов автоматизации; согласовывать архитектуры с TM Forum ANLET-фреймворком; Заниматься постановкой требований к данным RAN для платформы AN: интеграция источников (Common MR/MDT MR, PM/FM-счётчики, конфигурационные данные, geolocated метрики) с DataLake МТС и MWS; Внедрять метрики и KPI автономности (Autonomy Score, MTTR, share of automated changes, energy savings) в соответствии с TM Forum IG1218K; Сопровождать пилотные проекты автоматизации: подготовить техническое задание, приёмочное испытание, оценка бизнес-эффекта, тиражирование успешных сценариев на всю сеть; Взаимодействовать с вендорами поставщиками основного оборудования, ПАК и научно-исследовательскими партнёрами в части разработки и апробации новых алгоритмов автоматизации; Формировать требования к компетенциям RAN-инженеров и готовить программы переобучения в рамках перехода от manual-режима к supervised/autonomous операциям. Требования Высшее техническое образование (телекоммуникации, радиотехника, информационные технологии, прикладная математика); Опыт работы в области радиосетей 4G LTE / 5G NR не менее 7 лет, из них не менее 3 лет — на позициях, связанных с автоматизацией, SON или RAN-оптимизацией; Глубокое знание архитектуры RAN: eNodeB/gNB, протокольные стеки L1/L2/L3, интерфейсы S1, X2, Xn, F1, E1, O1/O2 (O-RAN); Практический опыт работы с SON-решениями. Настройка и оптимизация алгоритмов ANR, MLB, MRO, PCI, load balancing, coverage/capacity optimization; Опыт работы с инструментами планирования радиосетей: Atoll Forsk, Aircom Asset / Actix / Mentum Planet / TEMS Planet; Владение английским языком: деловая переписка, разговорный. Понимание методологии TM Forum Autonomous Networks (ANLET, IG1230, IG1218K): уровни автономности L0–L5, framework оценки зрелости, closed-loop процессы, intent-based operations; Будет плюсом опыт внедрения AI/ML-моделей в задачи RAN: прогнозирование, кластеризация, детекция аномалий, понимание жизненного цикла ML (MLOps); Будет плюсом опыт работы с большими объёмами данных: SQL, Python (уверенный уровень), Pandas/NumPy, Spark; BI-инструменты (Tableau/Power BI, Superset); Понимание методологии 3GPP TS 32.450 / TS 32.401 / TS 28.554 в части KPI/KQI RAN-сети. Условия программа ДМС с первого месяца работы - это 100% покрытие расходов в частных клиниках, включая стоматологию. Застраховать близких родственников можно со скидкой 50%. Для любителей путешествий - страховка в поездках за границу. А еще есть страхование жизни; очное и онлайн-обучение в Корпоративном университете, у внешних провайдеров, если это полезно для работы, обмен опытом во внутренних профессиональных сообществах, а также доступ к онлайн-библиотеке в полезными книгами и периодикой; оплачиваемые выходные на значимые события в жизни сотрудника; материальная помощь в сложных жизненных ситуациях; корпоративная мобильная связь + интернет, а так же единая подписка МТС Premium (онлайн кинотеатр KION, сервис МТС Music, МТС Строки и т.д.); предложения от партнёров для путешествий, развлечений и занятий спортом; возможность участия в волонтерских программах: мы организуем благотворительные ярмарки, образовательные проекты для детей, конкурсы социальных проектов и экологические проекты; внутренняя спортивная программа «В движении»: забеги, марафоны, турниры по разным видам спорта, медитации и занятия на баланс.

Проектирование контентных пайплайнов (аналог ETL). -Сбор идей/скриптов → Генерация промптов → Вызов API нейросетей (видео/аудио/изображения) → Автоматизированный рендер и монтаж (ffmpeg и аналоги) → Выгрузка готового ролика.

Location: Москва Мы ищем корпоративного архитектора в команду Департамента корпоративной архитектуры на проект создания корпоративного ИИ-помощника (B2E ассистент сотрудника) на базе взаимодействующих автономных агентов в целях повышения эффективности работы с внутренними сервисами для сотрудников. Первый этап отбора на эту вакансию - общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram или Макс. Диалог займёт примерно 10 минут. Его задача - уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным. Обязанности * проектирование концептуальной архитектуры функциональных областей по требованиям заказчика, кибербезопасности, инфраструктуры, надежности, архитектурных стандартов и других, определение целевых систем и поверхностей * определение архитектуры интеграции рыночных решений в архитектурный ландшафт Банка и между собой, выделение API * определение архитектуры мультиагентных решений для реализации AI-native инициатив * выполнение архитектурного сопровождения реализации задач: контроль отклонений от стандартов и архитектуры * анализ дублирования функционала, формирование стратегии развития решений * подготовка материалов и защита архитектуры решения на Архитектурном совете Банка и других, обеспечение полноты ведения архитектурного репозитория * проведение оценки и актуализация классов критичности внедренных АС/ФП, классов АС /ФП согласно критериев МинЦифры * регулярное исследование новых внутренних/рыночных технологических AI-решений и анализ возможностей их применения в целях стратегического развития решения * проведение исследований инноваций, предложение улучшений продукта и внутренних сервисов технологического решения, в рамках процедур закупки ПО подготовка архитектурных требований и анализ рыночных решений (заполненный чек-лист архитектурной оценки вендорских решений). Требования * практический опыт проектирования, разработки и внедрения комплексных мультиагентных решений в роли архитектора, ИТ-лидера, аналитика (как минимум одно успешно внедренное мультиагентное решение) * опыт проектирования распределенных решений в микросервисной архитектуре от 5 лет * экспертные знания современных алгоритмов машинного обучения, практик MLOps для мультиагентных решений, принципов работы LLM, A2A, MCP, RAG, guardrails, human-in-the-loop * понимание основных продуктов и бизнес-процессов в области внутренней деятельности предприятия: HR, ERP, ITSM, SRM и тд. * понимание процессов разработки и реализации сложных программных продуктов в крупных организациях * опыт управления изменениями в частности контроля внедрения изменений в сложных корпоративных информационных системах * навыки управления программами и проектами, опыт руководства проектными командами Будет плюсом: * навыки ведения переговоров, убеждении и продвижения идей * хорошие презентационные навыки, умение доносить и отстаивать свою позицию * аналитический склад ума, концептуальное и критическое мышление * высокая стрессоустойчивость, автономность и инициативность. Условия * место работы: м. Кутузовская; смешанный формат офис/удалено * график работы с 9:00 до 18:00 * оформление согласно ТК РФ. * социальный пакет: ДМС, спортзал, возможность обучения за счет компании, льготные условия кредитования * подписка Прайм с возможностью совместного использования на трёх близких.

Location: Islamabad, Pakistan Workplace: hybrid Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle Machine Learning Engineer , who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in computer vision is essential. Key Responsibilities Develop, deploy, and maintain machine learning models and systems Conduct in-depth data analysis, preparation, and processing Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing Follow established security guidelines and identify potential risks Collaborate with peers to review and ensure designs are effective, flexible, and reusable Solve issues and provide feedback for engineering design and delivery processes Ensure that features are deployed according to requirements and operate without unintended side effects Create and maintain comprehensive documentation for models and processes Monitor and support deployed solutions to ensure they meet performance expectations Actively seek out information and internal solutions to ensure reusability and adoption of common technologies Our expectations 3+ years of experience in the Field. Previous software engineering experience is preferrable Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data). Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics. Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle. Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes. The ability to design solutions using common design patterns and various design tools. Experience in experiment design and the ability to validate the credibility of information from various sources. A solid understanding of the business value of delivered features. Strong problem-solving skills, with an emphasis on using technical facts and reasoning. Excellent communication and collaboration skills, including active listening and the ability to build consensus. A strong drive for continuous self-development and a collaborative mindset for contributing to communities. Ability to balance quality and speed depending on the case, and justify your decision Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers English communication skills Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Cairo, Egypt Workplace: hybrid Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle Machine Learning Engineer , who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in computer vision is essential. Key Responsibilities Develop, deploy, and maintain machine learning models and systems Conduct in-depth data analysis, preparation, and processing Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing Follow established security guidelines and identify potential risks Collaborate with peers to review and ensure designs are effective, flexible, and reusable Solve issues and provide feedback for engineering design and delivery processes Ensure that features are deployed according to requirements and operate without unintended side effects Create and maintain comprehensive documentation for models and processes Monitor and support deployed solutions to ensure they meet performance expectations Actively seek out information and internal solutions to ensure reusability and adoption of common technologies Our expectations 3+ years of experience in the Field. Previous software engineering experience is preferrable Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data). Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics. Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle. Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes. The ability to design solutions using common design patterns and various design tools. Experience in experiment design and the ability to validate the credibility of information from various sources. A solid understanding of the business value of delivered features. Strong problem-solving skills, with an emphasis on using technical facts and reasoning. Excellent communication and collaboration skills, including active listening and the ability to build consensus. A strong drive for continuous self-development and a collaborative mindset for contributing to communities. Ability to balance quality and speed depending on the case, and justify your decision Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers English communication skills Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Cairo, Egypt Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Senior Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 5+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Islamabad, Pakistan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Senior Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 5+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Senior Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 5+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Islamabad, Pakistan Workplace: hybrid The AI Solution Delivery team is expanding its capabilities in NLP to build production-grade AI products. We are looking for a Senior Data Scientist to architect, build and deploy complex models for automating customer support operations. You will be responsible for selecting the right technical approaches and ensuring the scalability of our solutions. A proven track record in applying advanced NLP techniques to real-world problems is required. Key Responsibilities Lead the entire machine learning model lifecycle, from initial research and hypothesis testing to production deployment and maintenance Translate complex business goals into well defined data science problems and quantifiable metrics Design and develop robust, scalable machine learning systems from scratch, including data analysis, annotation, and processing pipelines Contribute to the overall system architecture and integrate ML models with existing backend services and infrastructure Monitor and maintain deployed models, proactively identifying and addressing issues like concept drift to ensure consistent performance Support the development and growth of other team members through mentorship and participation in onboarding programs Drive continuous improvement by automating repetitive tasks and proposing innovative solutions that lead to significant business impact Communicate complex technical concepts and findings clearly and concisely to both technical and non technical stakeholders Skills, Knowledge and Expertise 5 years of professional experience in a data science or machine learning role with deep focus on NLP. Previous software engineering experience is preferrable. An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus Expert level proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit learn). Deep expertise in classic machine learning and deep learning techniques, with a strong understanding of advanced mathematics relevant to these fields. Experience with ML system design and MLOps practices for building, testing, deploying, and monitoring models in a production environment. Proven experience with event systems, deployment environments, and maintaining production services. Familiarity with technologies for streaming, batch, and async data processing. Strong understanding of software system design principles and the ability to contribute to architectural discussions. Experience in experimental design to validate hypotheses and measure the effectiveness of solutions. A solid grasp of security, risk, and control concepts in a production environment. Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle Machine Learning Engineer , who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in computer vision is essential. Key Responsibilities Develop, deploy, and maintain machine learning models and systems Conduct in-depth data analysis, preparation, and processing Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing Follow established security guidelines and identify potential risks Collaborate with peers to review and ensure designs are effective, flexible, and reusable Solve issues and provide feedback for engineering design and delivery processes Ensure that features are deployed according to requirements and operate without unintended side effects Create and maintain comprehensive documentation for models and processes Monitor and support deployed solutions to ensure they meet performance expectations Actively seek out information and internal solutions to ensure reusability and adoption of common technologies Our expectations 3+ years of experience in the Field. Previous software engineering experience is preferrable Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data). Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics. Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle. Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes. The ability to design solutions using common design patterns and various design tools. Experience in experiment design and the ability to validate the credibility of information from various sources. A solid understanding of the business value of delivered features. Strong problem-solving skills, with an emphasis on using technical facts and reasoning. Excellent communication and collaboration skills, including active listening and the ability to build consensus. A strong drive for continuous self-development and a collaborative mindset for contributing to communities. Ability to balance quality and speed depending on the case, and justify your decision Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers English communication skills Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

• Опыт руководства ML- или LLM-платформой либо инфраструктурной командой. • Практическое понимание LLM-инфраструктуры: serving (vLLM, TGI, Triton или аналоги), ограничения и утилизация GPU.

Location: Anywhere in France, Belgium, Spain HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. ⭐ THE ENGINEERING TEAM ⭐ In our engineering team, we build the infrastructure, interfaces, and applications to provide first-class service to our members, health professionals, and even ourselves! Being an engineer at Alan means joining a team of talented, committed and passionate engineers, with a lot of product interaction. We move fast, with a lot of ownership, and are proud to tackle big problems! Our process is very simple: those who make product decisions are the same ones who build them. Want to know more? Read this article https://blog.alan.com/tech-et-produit/engineering-organisation-at-alan if you want to discover how life as an Engineer at Alan and this article https://medium.com/alan/engineering-career-path-at-alan-e5de5c2e740c on our Engineering career pat 🛠️ THE STACK Python services on FastAPI, async SQLAlchemy and PydanticAI, Temporal for durable execution, PostgreSQL, MCP, Daytona sandboxes, various LLM model providers, Terraform on AWS, React with Vite. Our main backend is a Flask monolith. We deploy daily and believe in distributed ownership: you build it, you own it. 🤖 THE AGENTIC PLATFORM AREA In 2027, we expect to run more AI agents than we have employees. Agents already do real work at Alan. They answer members' health questions, resolve support requests, read medical documents, detect fraud, automate operational tasks, and write a large share of our code. But every team building an agent ends up solving the same hard problems again: how to call models safely with health data, how to give an agent tools and the right permissions, how to know whether a prompt change made it better or worse, how to debug what it did. The result: several agent frameworks and evaluation stacks, each with its own security and compliance story. The Agentic Platform area was created to fix that. Our mission: make it cheap, fast and safe for every Alaner or crew to build, run and improve agentic products and workflows. We own the environment agents run in (infrastructure, runtime, evaluation, observability, skills lifecycle), with security, privacy and compliance by design, and the generalist products on top: Hopper and the Agent Studio. Product teams and all Alaners own their agents, skills, and their outcomes. We give them the paved road. Two crews, one team: - Hopper is every Alaner's AI companion. Five months after it started, 80% of Alaners use it, 3k sessions are created each day, and about 30% of our pull requests are opened from it (watch how we built it https://www.youtube.com/watch?v=hAu7Ngfd0ys). - AI Product Platform builds the shared blocks every AI product at Alan runs on. For instance, our ops teams now build agents autonomously on our Ops AI Platform (read how https://medium.com/alan/how-we-bootstrapped-an-ai-agents-platform-for-operations-at-alan-fefd87ed6314, watch the talk https://www.youtube.com/watch?v=_pg1ij8k5Rw). 🧩 PROBLEMS YOU'LL ONLY FIND HERE Run agents at scale - 🧠 One runtime for every AI product. A shared agent framework, from a single model call to multi-agent workflows with live streaming, and one gateway to every model: routing, failover across providers, quotas and cost per use case built in. - ⏳ Agent sessions that last for hours. Durable sessions, started from the web, Slack, Linear or GitHub, that survive pod restarts and deploys, and pick up exactly where they left off. - 📦 Sandboxes at scale. An isolated environment for every agent session and every evaluation, on an infrastructure compliant with sensitive data, with negligible boot overhead. Make agents trustworthy and proactive - 🔌 One tool gateway to the whole company. An MCP gateway that connects agents to internal tools and data, external services, computer use, managing identity and credentials. - 🔐 Agents safely acting on critical data. We give each agent no more than the access it needs. Every tool call goes through identity, permissions, confirmations and audit. No sensitive data leaks. - 🧪 Evals you can replay in time. Re-run an agent or skill against production as it was at a past point in time, on real data, safely isolated. Anyone can ship knowing their change is an improvement rather than a regression. - 🔁 Agents and skills that proactively fix themselves. Every run is traced, and every human correction, failed tool call or escalation becomes a prioritized fix that Hopper can apply. Put agents in everyone's hands - 📚 Skills as a product. A registry where Alaners and teams publish, share and version their skills, so that nothing needs to be repeated twice. - ✨ AI that non-engineers can own. Alaners who ship code through Hopper, Operators or Sales who build their own agents or skills in our Studio and operate them from their day-to-day tools. - ⏰ Automations and artifacts. Agents that run on a schedule or on an event, and build the dashboards and hosted sites that become the go-to places for collaboration. ⭐ IS IT YOU WE’RE LOOKING FOR? What you'll do - Own some of the problems above end to end, from design to production, with significant influence on architecture and code quality - Work closely with the people who use what you build: Alaners, product teams, ops - Own reliability, cost and security in production - Mentor engineers, contribute to our product and engineering culture, and help build the team Experience we value - 3+ years in software engineering, with solid backend fundamentals - Platform-leaning or product-leaning: both are welcome. Depth in at least one of: distributed systems and runtimes, evaluation and data quality, security and authorization, product UX - A good sense of user and developer experience, whatever you build - You built products, platforms or infrastructure used by other people - Experience with LLM APIs, agent frameworks, evals or MCP is a strong plus; ML or MLOps experience is a plus Mindset we value - You measure success by adoption, not by what is available - You make concrete tradeoffs between speed, reliability, cost, security and user experience - You simplify complexity with clear abstractions and maintainable code - You care about whether people can understand and verify what an AI system did - You keep up with the latest AI technology and know the open-source AI ecosystem - Self-directed, pragmatic, humble, collaborative, enthusiastic about learning and teaching - Excited about AI changing who can build, investigate and automate work Above all, we look for high potential and curiosity: make sure to show us this when you apply. Everything else is a bonus. You should be fluent in English (no French required). 🌍 HOW WE WORK - Location: you must be legally eligible to work in France, Belgium, or Spain. - Remote work: we offer remote work flexibility, but we value in-person collaboration. 🙌 PERKS & BENEFITS https://alan.com/en/careers/a/salary-equity At Alan, we believe that being in good health is a basic need, and it starts with our employees. This is why Alaners are provided with a stimulating environment and perks ensuring they are happy, efficient and spend only high-quality time with co-workers. Therefore, we offer: - Fair rewards. Generous equity packages complement your base salary. - Flexible Office. Amazing office space at our HQ, sponsored co-working hubs or a full-remote experience with home office equipment sponsorship, we want you to live where you’re the happiest. - All the tools you need. Top of the range equipment: Macbook Pro, keyboard, laptop stand, monitor, and noise-canceling headphones. - Flexible vacation policy and flexible working hours. Organize your time as you wish. - Delightful healthcare insurance: Extremely comprehensive health insurance - 100% for you and your children, 90-100% for partners depending on your country (permanent contracts only). - Transport. Country-specific commuter benefits. - Learning & Training opportunities. A highly flexible Training policy free books and budget to attend and speak at conferences if the opportunity arises. - Personal growth through coaching: At Alan, coaching isn't just a perk - it's core to who we are. Every Alaner is paired with a dedicated coach from day one, who helps maximize their impact, nurture engagement, and navigate Alan's values to develop their full potential. Learn more about our coaching culture https://alan.com/en/blog/discover-alan/a/alan-culture-excellence-through-coaching. - Parental leave. Extended parental leave for all new parents.   IMPORTANT NOTE: WE HIRE PEOPLE, NOT ROLES. If you're excited about this opportunity but don't check every box, we'd love to hear from you. Everyone, no matter how underrepresented, should feel free to apply as it can only bring learnings or success. If you identify yourself as a woman: Did you know that research shows women often apply only when meeting 100% of requirements? Remember, this is just a guide, not a checklist. We'll be thrilled to receive your application! 🔖 Check out our About Alan https://alan.com/about and Career https://alan.com/careers pages, as well as our Medium https://medium.com/alan, blog https://blog.alan.com/ and Glassdoor page https://www.glassdoor.com/Overview/Working-at-Alan-EI_IE1696043.11,15.htm for more info. 🔄 A quick note about the process Note: While you're applying for a specific area, you may join a different engineering team based on where we think you'll have the most impact. Check out descriptions of other areas here https://www.notion.so/alaninsurance/Software-Engineering-Areas-Alan-2c61426e8be7805bacd2cdbc08c32640?source=copy_link.

Location: Москва Дивизион "Моделирование и исследование данных" отвечает за компетенцию Data Science в Блоке. Мы разрабатываем и внедряем end-to-end решения на основе машинного обучения в бизнес-процессы Сети продаж банка. Сейчас мы ищем в команду MLE Data Scientist’а. Обязанности * Ведение всех этапов AI-проекта: от постановки задачи и требований с бизнес-заказчиком до внедрения и мониторинга; * Разрабатывать с нуля AI-агентов и микросервисы для AI-продуктов; * Разрабатывать алгоритмы взаимодействия ML-моделей и сервисных модулей; * Принимать участие в разработке архитектуры и дизайна агентов, ML приложений и AI-сервисов; * Проектировать и разрабатывать backend-части наших систем; * Стэк: 50% Classic ML + 50% Agents. Требования * Компетенции: Python, DE, ML (Classic ML + NLP), Statistics, LLM/Agents, MLOps; * Развитые soft-skill для общения с заказчиком; * Понимание принципов разработки, внедрения и сопровождения AI-решений; * Уметь писать хороший и понятный пром-ready код; * Опыт промышленной разработки на python от 2-ух лет; * Хорошей знание алгоритмов, структур данных, GIL, async; * Опыт работы с реляционными и нереляционными базами данных, понимание границ применимости каждого вида из них; * Опыт построения отказоустойчивых высоконагруженных распределенных систем; * Будем иметь дело с широким набором инструментов и технологий: Python, Elastic, Kafka, FastAPI, Docker, OpenShift, SQL, Spark, Hadoop, GPU, BitBucket. Условия * Полное соблюдение ТК РФ, льготные условия по программам страхования, кредитования для сотрудников Банка;) * График 5/2, офис на м. Кутузовская, гибрид; * Достойный уровень вознаграждения (оклад + внушительная годовая премия); * ДМС с первого дня работы и льготная мед. страховка для близких родственников; * Социальная поддержка сотрудников; * Возможность стать частью команды, реализующей программы цифровой трансформации банка - на основе передового опыта и инновационных идей; * Широкие возможности для профессионального развития: корпоративный университет и множество онлайн-программ обучения. Корпоративное обучение за счет компании; * Бесплатная подписка СберПрайм с возможностью подключения 3 родственников, многочисленные скидки и бонусы от партнеров: СберМаркет, МегаМаркет, Самокат, Еаптека и др.; * Корпоративная пенсионная программа

Location: Москва Дирекция мониторинга Agile занимается созданием дашборда для руководителей в Сбере. Мы собираем данные по всем процессам в Банке, анализируем их и предоставляем необходимую информацию для принятия решений. Ищем в команду DS уровня Senior или кто готов выступить в роли Lead DS, который будет участвовать в полном цикле разработки – от формирования идеи до вывода ML решений в промышленную эксплуатацию (ПРОМ). Вы будете строить ИИ-агентов и ML модели, которые работают с ключевыми процессами и влияют на опыт тысяч сотрудников Сбера. Обязанности ### Обязанности: * погружение в нюансы процессов и участвовать на встречах с пользователями * активное взаимодействие с командами разработки и DS * разработка прототипов pipeline сбора и обработки данных * разработка CI/CD пайплайнов, оборачивание кода в docker образы, развертывание контейнеров в Kubernetes, управлять секретами и ролевыми доступами * fine-tuning моделей (instruction-tuning, adapters, LoRA, SFT, LLM-RL) * создание, внедрение и развитие MLOps-практики (стенды, процессы, DevOps), инференса LLM (среды разработки, тестирования и инференса моделей в различных режимах работы (Batch, Streaming) и использование ресурсов (CPU, GPU)) * обеспечение мониторинга и выявление узких мест в работе систем. Требования ### Требования: * опыт коммерческой разработки ML-решений / AI агентов от 4-5 лет * полный цикл от идеи до production и понимание, как обеспечить надежность моделей * практический опыт и представление в RAG-системах, мульти агентных приложениях, fine-tuning LLM и prompt инжиниринге * опыт использования Airflow и MLFlow (или другими аналогичными инструментами) * автоматизация процесса мониторинга качества моделей и их калибровка * уверенное знание MLOps практик * уверенное владение ИИ-инструментами. Условия ### Условия: * комфортный современный офис на м. Кутузовская, (офисный режим работы) * ежегодный пересмотр зарплаты, квартальной и годовой премии * уникальная система обучения Сбера для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС и льготное страхование для семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью использования на трех близких * вознаграждение за рекомендацию друзей в команду Сбера.