Вакансии

Ищем ML-инженера с сильным практическим опытом работы с LLM: fine-tuning, post-training, evaluation и inference. Важно: это не позиция классического ML Engineer / Data Scientist. Нужен кандидат, который самостоятельно обучал и адаптировал LLM и понимает полный цикл — от данных и обучения до evaluation и deployment. Задачи: - Разработка и улучшение пайплайна LLM post-training: SFT, preference optimization, Offline / Online RL; - Улучшение instruction following и построение системы benchmarking / evaluation; - Обучение LoRA / QLoRA-адаптеров под различные домены и задачи; - Работа с reward models / verifiers и reward functions для RL; - Разработка RAG-систем: retrieval, reranking, context construction и evaluation; - Обучение и интеграция tool use / function calling / structured outputs; - Оптимизация inference и serving моделей с использованием vLLM / SGLang; Обязательные требования: - Практический опыт fine-tuning / post-training LLM; - Опыт с SFT и preference optimization: DPO / IPO / ORPO / SimPO или аналогами; - Опыт или хорошее практическое понимание Online RL для LLM: GRPO / GSPO или аналогичных методов; - Понимание reward models, preference data и LLM evaluation; - Уверенное владение Python, PyTorch, Hugging Face Transformers; - Опыт с TRL и/или verl; - Опыт с LoRA / QLoRA / PEFT; - Опыт inference / serving через vLLM и/или SGLang; - Опыт разработки RAG, tool use или structured generation; - Понимание архитектуры современных LLM и особенностей их обучения и inference; - Опыт работы с GPU, distributed training и Linux / Docker / Git; - Умение самостоятельно ставить эксперименты, выбирать метрики и анализировать результаты. Будет плюсом: - Continual / domain pre-training; - Reasoning RL; - Multilingual LLM или code generation; - FSDP / DeepSpeed / Megatron-LM; - Quantization и оптимизация inference.

Задачи: — Подготовка тест-кейсов на основании требований и критериев приемки инициатив — Функциональное и интеграционное тестирование ИИ-агентов, workflow на платформе Dify, API-интеграций со внутренними системами — Проверка позитивных, негативных и пограничных сценариев, включая нетипичные запросы пользователей и «галлюцинации» модели — Регрессионное тестирование после изменений промптов, моделей, базы знаний (RAG) и интеграций — Проверка соответствия результата согласованным требованиям и метрикам качества — Формирование и поддержание тестовых выборок (golden set) совместно с бизнесом и ML-командой, участие в оценке качества ответов — Фиксация дефектов, контроль их устранения, отчётность по качеству релизов — Проведение приемочного тестирования перед выпуском в production и участие в приемке с бизнесом Требования: — Опыт работы от 3 лет в тестировании ПО — От 1 года тестирования продуктов на основе ML/LLM или тестирования API и интеграций backend-сервисов — Понимание специфики недетерминированных ответов модели — Не менее 2 проектов тестирования интеграционных / API-решений или ИИ-продуктов с выводом в промышленную эксплуатацию — Опыт построения регрессионных наборов и тестовых выборок — Желательно: опыт в e-commerce, ритейл, B2B-дистрибуция, клиентский сервис — Тест-дизайн, тестовая документация, работа с баг-трекером — Тестирование REST API (Postman или аналоги), чтение JSON, логов — SQL на уровне выборок для проверки данных — Python на базовом уровне для скриптов проверки и подготовки тестовых данных — Понимание принципов работы LLM, RAG, промптов и метрик качества ИИ (точность, полнота, оценка ответов по критериям) — Желательно: опыт работы с платформами оркестрации LLM (Dify, LangChain и т.п.) — Желательно: автоматизация тестирования API (pytest, requests) и LLM-eval инструменты (RAGAS, DeepEval, promptfoo или аналоги) — Желательно: опыт разметки и оценки ответов модели (LLM-as-a-judge, ручная оценка по рубрикам) — Желательно: CI/CD, Git на базовом уровне — Системы учета задач и документации: Jira / Yandex Tracker, Confluence / Yandex Wiki, Git, реестр ИИ-инициатив в Google Sheets Условия: — Занятость: фултайм — Формат работы: удалённая по РФ или в офисе в Москве/Саранске (hybrid) — Тип контракта: ГПХ, ИП — Оплата до 1400 руб./час гросс

Location: Москва Мы - направление Сбербанк Онлайн и AI-помощник в дивизионе Премиальный сегмент. Наша общая цель – развивать интеллектуальные сервисы для повышения качества обслуживания премиальных клиентов и эффективности работы сотрудников. Одно из ключевых направлений – комплексный AI-анализ коммуникаций менеджеров с клиентами: анализ транскрибированных диалогов и аудиосигнала, контроль стандартов обслуживания, выявление эскалаций и пропущенных элементов, анализ тональности и речевых характеристик, а также формирование рекомендаций для сотрудников и руководителей. **Почему мы:** ·      Развиваем прикладные AI-решения, которые быстро проверяются на реальных бизнес-сценариях; ·      Есть возможность работать не только с LLM и prompt-based подходами, но и с классическим ML, NLP, Speech и собственными моделями; ·      Доступна внутренняя инфраструктура для обучения моделей и использования GPU-ресурсов; ·      Можно участвовать в развитии решений на базе GigaChat, RAG, LoRA и агентных подходов; ·      Небольшая AI-команда: заметное влияние на архитектуру, эксперименты и итоговый продукт; ·      Сильное бизнес- и IT-окружение, высокие темпы вывода решений в эксплуатацию. Обязанности ·  разрабатывать и поддерживать высоконагруженные Python-сервисы (FastAPI), обеспечивающие работу AI-компонентов · проектировать и разрабатывать REST/gRPC API для интеграции AI-моделей с бизнес-системами; · строить надежные data-pipeline: от приема сырых данных (Kafka) до выдачи результатов в PostgreSQL и внешние системы; · работать с асинхронным кодом (asyncio) для обработки потоков данных в реальном времени · интегрировать LLM и RAG-сервисы в продуктовый контур через API · готовить данные для экспериментов, формировать обучающие и валидационные выборки · участвовать в Code Review, технических обсуждениях, документировании решений и выводе сервисов в промышленную эксплуатацию. Требования · уверенное владение Python (3.9+) для разработки высоконагруженных бэкендов, понимаете GIL, асинхронность и многопоточность · практический опыт с FastAPI, Pydantic, SQLAlchemy/asyncpg, asyncio и разработкой REST/gRPC API · умение проектировать и оптимизировать схемы PostgreSQL, писать сложные SQL-запросы, использовать индексы и понимать принципы работы MVCC · опыт работы с брокерами сообщений (Kafka/RabbitMQ), понимание моделей доставки, партиционирования и идемпотентности · понимание принципов контейнеризации (Docker) и базовых практик CI/CD · умение писать надежный, покрытый тестами код (pytest, coverage) и проводить code review · понимание принципов работы LLM, prompt engineering, RAG и подходов к оценке качества генеративных моделей · умение проводить воспроизводимые эксперименты и аргументированно сравнивать несколько подходов к одной задаче · самостоятельность, готовность разбираться во внутренней инфраструктуре и ограничениях корпоративного контура · умение объяснять результаты экспериментов разработчикам, аналитикам и бизнес-заказчикам. **Будет плюсом:** ·      опыт Speech/Audio ML: acoustic features или анализ эмоций по аудио ·      опыт в NLP: классификация текстов, embeddings, transformers, извлечение признаков или близкие задачи ·      опыт fine-tuning LLM, LoRA, работы с embeddings и векторным поиском ·      опыт с CatBoost/LightGBM и predictive ML-задачами ·      опыт разработки или эксплуатации ML-сервисов, контейнеризации и оркестрации ·      опыт работы в крупных корпоративных контурах с повышенными требованиями к безопасности. Условия ·      гибкий график начала рабочего дня ·      уровень заработной платы определяем по итогам собеседования, отталкиваемся от ваших ожиданий ·      команда профессионалов своего дела ·      возможность развития по направлениям ·      ДМС с 1-го дня работы ·      обучение и сертификация за счет компании (в том числе обучение английскому языку компенсируется) ·      скидки от партнеров и корпоративные программы ·      рыночная заработная плата и годовая премия по итогам ·      выстроенные процессы по адаптации, оценке и понятную систему роста в компании; ·      наставник на период адаптации и корпоративную жизнь, в которой можно участвовать по желанию ·      сплоченная команда, agile-практики и помощь в профессиональном развитии.

Задачи: — Управлять дорожной картой Help Center, включая качество поиска, стратегию статей, AI-резюме и потоки самообслуживания для миллионов клиентов по всему миру — Повышать уровень автоматизации и удовлетворённости клиентов (CSAT), выявляя возможности для снижения обращений через улучшенное самообслуживание, умный показ и проактивное решение — Создавать интегрированные с AI поисковые и контентные решения совместно с партнёрами по ML/LLM для повышения релевантности, резюме и контекстных быстрых действий — Сотрудничать с командами CX Ops, Content Design, Engineering и Data Science для поддержания точных и полноценных знаний на разных языках и регионах — Определять и отслеживать метрики воронки (Automation Rate, Search AtR, CSAT, уровень эскалации) и использовать данные для приоритизации — Работать с внутренними платформенными командами и сторонними поставщиками для обеспечения бесшовной интеграции чата, голоса и инструментов агентов Требования: — Опыт работы в управлении продуктами не менее 5 лет с продуктами, ориентированными на клиентов, платформами контента или продуктами самообслуживания — Опыт создания или улучшения поиска, управления знаниями или самообслуживания, желательно с AI/ML-поиском, рекомендациями или RAG retrieval — Умение определять и использовать метрики поддержки/самообслуживания (автоматизация, снижение обращений, CSAT, эскалация) для принятия решений — Сильные навыки кросс-функционального взаимодействия с командами контента, инженерии, операций и аналитики без владения полным UX — Опыт работы с платформами Help Center или базами знаний (Contentful, Zendesk, Freshdesk, Salesforce или аналогичные CMS/инструменты поддержки) — Ответственное использование генеративного AI с сохранением человеческого контроля для обеспечения готовых к бизнесу результатов и улучшения эффективности, затрат и качества рабочих процессов Условия: — Формат работы: удалённо (Remote - India) — Равные возможности для всех кандидатов без дискриминации — Возможность получить разумные приспособления для лиц с ограниченными возможностями по запросу Дополнительно: — Кандидаты могут подать максимум 3 заявки в течение 6 месяцев — Соблюдение политики конфиденциальности и арбитража споров при подаче заявки

Location: San Francisco, CA; New York, NY; Toronto Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Sierra was co-founded by Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/ who first met while working together at Google over 20 years ago. Bret currently serves as Chairman of the Board of Open AI. Previously, he was co-CEO of Salesforce and CTO of Facebook. Clay spent 18 years at Google, where he most recently led Google Labs. WHAT YOU’LL DO - Design and deliver production-grade AI agents https://sierra.ai/blog/meet-the-ai-agent-engineer: You'll build and ship highly performant, reliable, and intuitive AI agents that are central, mission-critical and drive revenue directly to Sierra's growth. These aren't prototypes—they are powerful, scalable systems running in production environments across industries like finance, healthcare, and commerce. - Drive the Agent Development Life Cycle https://sierra.ai/blog/agent-development-life-cycle (ADLC): You'll have complete ownership and autonomy from initial pilot through deployment and continuous iteration. You'll be responsible for building, tuning, and evolving AI agents in production environments, defining the standard for ADLC best practices along the way. - Partner with large enterprises and cutting-edge startups: You’ll work directly with leaders at some of the world’s largest enterprises to understand their most pressing business challenges, and build AI agents that transform how they operate at scale. You'll also partner with the most cutting-edge startups in Silicon Valley, embedding AI agents across their entire business stack to drive innovation and efficiency. - Build the future of the platform: Your direct work with customers will guide the evolution of Sierra's core platform. You'll surface unmet needs, prototype new tools and features, and collaborate with research, product, and platform to shape the future of AI agent development and Sierra's product. EXAMPLE PROJECTS These are some examples of projects that engineers on our team have worked on recently: - Design and build AI agents for large telecommunications and media companies that consistently outperform human agents in managing subscription churn - Develop and refine AI agents capable of navigating complex customer interactions, like troubleshooting a broken device and personalizing product recommendations - Create generalizable AI agent frameworks tailored for industry-specific use cases. See some examples in our financial services blog https://sierra.ai/blog/five-ways-to-use-ai-agents-financial-services! - Facilitate design partnerships for new product initiatives, such as new agent architectures, self-service capabilities, and generative agent development - Experiment with the latest voice models and figure out how to integrate them at scale to enterprise-grade customers WHAT YOU'LL BRING - Language fluency - professional fluency in both Spanish and English - Experience building and scaling end-to-end production systems - Strong technical problem-solving skills, especially in fast-changing, ambiguous environments - A builder and tinkerer’s mindset with high agency - you find creative ways to overcome obstacles and ship - Comfort working directly with customers to understand their needs and solve real-world problems - Excellent communication skills - clear, direct, and persuasive across technical and non-technical audiences EVEN BETTER... - Experience building or deploying AI/LLM systems in production - Have been a founder or founding engineer - you know what it means to balance craft, ownership, and speed - Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering - Prior experience with React, TypeScript, and/or Go - Previous roles where you interfaced with customers or led technical projects with external stakeholders OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits - Lunch, as well as delicious snacks and coffee to keep you energized when working onsite - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region and employment type, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply through the link on this page even if your experience doesn't precisely match the job description. Sierra is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state or local laws, including veteran status and disability status. Hiring decisions are made by people, based on merit. If you need a reasonable accommodation for a disability, a religious practice, pregnancy or any related condition at any point in the application or interview process, contact us at accommodations@sierra.ai. Requests are handled confidentially and do not affect your candidacy. We will consider qualified applicants with arrest and conviction records in accordance with applicable fair chance laws.

Задачи: — Разрабатывать и совершенствовать платформу данных электронной коммерции нового поколения — Проектировать и разрабатывать ETL пайплайны на Airflow для Spark (Batch, Streaming), Iceberg, Trino — Оптимизировать действующие пайплайны и джобы на Spark, мониторить нагрузку на кластер и оптимальность ресурсов, при необходимости оптимизировать — Взаимодействовать с аналитиками и ML специалистами для создания и развертывания инструментов и наборов данных, поддерживать связи с data ops для развития дата-платформы — Анализировать и внедрять инструменты инжиниринга данных (Lineage, DQ) — Проектировать хранилища данных на основе Kubernetes, настраивать производительность, мониторинг, планировать емкости кластера и инфраструктуры — Решать задачи, связанные с внедрением облачной инфраструктуры, разрабатывать новую архитектуру хранилища — Проводить code review и участвовать в архитектурных решениях по дата платформе Требования: — Понимание ресурсной модели Kubernetes, просмотр конфигураций, troubleshooting — Опыт работы с Hadoop (HDFS, Hive Metastore) — Опыт работы со Spark (spark SQL, streaming, понимание нюансов работы с pyspark) — Владение процедурами, snapshots, maintenance Apache Iceberg — Глубокое понимание Airflow (версии 2, 3), включая идемпотентность, assets, backfills — Уверенное знание Git, CI/CD — Знание Python, Scala/Java, ООП, проектирование библиотек, интеграционные и юнит тесты — Хорошее понимание СУБД, SQL Будет плюсом: — Опыт работы с Docker для поднятия локального окружения проекта — Опыт работы с Kafka, понимание архитектуры и слабых мест — Опыт с облачными технологиями, преимущественно Yandex Cloud — Опыт с Clickhouse — AI-native подход к разработке: использование Claude Code / Codex и подобных инструментов, опыт построения harness-систем — агентов с инструментами, автоматизации через LLM, RAG

Задачи: — Анализировать данные, выбирать подходящие архитектуры (от классического ML до нейросетей) и обучать модели — Искать и тестировать новые подходы и решения, адаптируя их под наши нужды — Самостоятельно разворачивать модели в продакшн-среде — Настраивать рабочее окружение и библиотеки в условиях отсутствия прямого доступа к интернету — Настройка и интеграция локальных LLM Требования: — Уверенное владение Python и ML-стеком (Pandas, Scikit-learn, PyTorch/TensorFlow) — Понимание математики и статистики, стоящую за алгоритмами — Опыт работы с современными архитектурами (NLP, CV или LLM) — Умение упаковывать свои решения в Docker (критично для нашего контура) — Базовое знание Linux и опыт построения API-сервисов — Готовность работать полностью оффлайн в защищенной среде Будет плюсом: — Опыт развертывания локальных языковых моделей (LLM) — Навыки оптимизации кода под конкретное железо (GPU) — Умение работать с SQL-базами данных — Опыт с RAG, inference/serving Условия: — Только оффлайн формат работы (строго закрытый контур) — Работа в стабильном, регламентированном стеке с использованием собственных мощностей (GPU) — Работа с уникальными данными в защищенной среде

Задачи: — проводить функциональное, регрессионное, smoke, UAT и негативное тестирование; — тестировать API через Postman / Insomnia; — писать тест-кейсы, чек-листы и баг-репорты в Jira / Zephyr; — работать с SQL, логами и трейсингом; — тестировать AI-чатов и multi-turn сценарии; — проверять edge cases, adversarial / jailbreak-промпты и safety-фильтры; — оценивать качество ответов LLM: точность, релевантность, полноту и тональность; — тестировать RAG-пайплайны и корректность источников в ответах; — использовать AI-инструменты для автоматизации рутинных QA-задач и написания небольших скриптов. Требования: QA-база: — от 3 лет опыта в ручном тестировании; — понимание основных видов тестирования; — Jira / Zephyr; — SDLC / STLC, Agile / Scrum; — Postman / Insomnia; — базовый SQL; — работа с логами и трейсингом. AI / LLM: — понимание принципов работы LLM: токены, temperature, top-p, context window; — понимание prompt engineering; — знание типичных проблем LLM: hallucinations, потеря контекста, nondeterminism, sensitivity к формулировкам; — опыт или понимание тестирования multi-turn диалогов, edge cases и jailbreak-промптов; — понимание оценки качества AI-ответов; — базовое понимание RAG: чанкинг, эмбеддинги, vector search, reranking, retrieval; — умение проверять корректность источников и цитирования в RAG-ответах. Технически: — практический опыт с Claude Code или аналогичными AI coding CLI; — базовое понимание ООП; — способность читать код автотестов и писать простые вспомогательные скрипты на Python / Java / C#. Условия: — удалённая работа; — занятость full-time; — тип оформления: ГПХ / ИП; — зарплата обсуждается индивидуально; — часовой пояс GMT + 3.

Задачи: — Разработка и поддержка AI-архитектуры: интеграция API (OpenAI, Anthropic), создание кастомных RAG-систем, оптимизация промптов и пайплайнов данных. — Быстрое UI/UX прототипирование: самостоятельное создание аккуратного, чистого фронтенда/интерфейса для разработанной AI-логики. Требования: — Английский язык на уровне B2 и выше (Upper-Intermediate), свободное чтение документации и понимание задач без перевода. — Готовность работать по схеме выплат Post-paid (в конце отчетного периода после закрытия инвойса заказчиком). — Наличие статуса самозанятого/ИП или готовность его открыть, умение выставлять инвойсы для европейского контрагента. — Наличие GitHub с реальными, работающими AI-кейсами (RAG, агенты, интеграция LLM). Условия: — Работа полностью удалённая, без микроменеджмента и созвонов, все коммуникации с клиентом ведёт Delivery Manager. — Фиксированный бюджет $5,000 в месяц. — Оплата через чистый блокчейн-расчет (USDT / USDC) без задержек.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Location: San Francisco, CA What you will do as a Customer Engineer The Customer Engineer (CE) is the core driver of our technical go-to-market strategy: a trusted technical advisor and primary architect of the customer journey. You will bridge the gap between technical strategy and business value—guiding enterprise clients from initial pre-sales technical validation through onboarding, deployment architecture, and full lifecycle expansion. Sitting within the GTM organization and reporting to a Customer Engineering Leader, you will not hand off your customers once an initial agreement is reached. Instead, you will operate as a quota-carrying technologist, leveraging AI-augmented workflows to eliminate administrative toil so you can focus on high-value architectural design and driving measurable business outcomes across the entire customer journey. Key Responsibilities Technical Validation: Partner closely with Account Executives to lead technical deep-dive discovery, capture business requirements, and map complex customer infrastructure - DNS, CDN, Zero Trust, Network Security, Workers - to fit-for-purpose Cloudflare architectures and bespoke solutions. . Define, scope, success criteria, and technical evaluations plans for Proof of Concepts (PoCs) and Technical Validations. Deliver tailored technical presentations, architectural diagrams, and demonstrations that show how Cloudflare solves specific performance, security, and reliability challenges. Proactively address complex technical objections, security compliance questions, and edge-case architectural requirements during the sales cycle. Take accountability for a suitable solution deployment strategy (Customer, Partner or Cloudflare Professional services) being an integral part of all proposals. Customer Adoption & Value Realization: Provide oversight to technical onboarding and implementation, but not execute the implementation, serving as the primary executive and engineering contact for the customer. Design structured migration and onboarding blueprints tailored to the customer’s environment (e.g. DNS migrations, Zero Trust rollouts, WAF tuning guidance), and act as the lead consultant for customer engineering teams, walking them step-by-step through configuration best practices.  Partner with Account Executives to own discovery, demos, onboarding, technical health, risk, and expansion strategy. Proactively monitor customer health scores and telemetry to ensure rapid time-to-value, resolve integration bottlenecks, and reduce deployment risk. Commercial Accountability & Expansion: Carry a variable quota tied directly to new business acquisition and/or ongoing account growth. Be commercially accountable for ensuring deep platform adoption, retaining revenue, and identifying strategic cross-sell and up-sell opportunities based on product usage data. Lead technical Quarterly Business Reviews (QBRs) to translate platform capabilities into return on investment for C-level executives. Trusted Advisory: Act as a trusted technical advisor throughout the entire customer lifecycle, maintaining a continuous, deep relationship with key stakeholders from initial onboarding through maturity, renewal, and expansion. Provide thought partnership on architecture, AI control layers, security posture, and deployment confidence. Identify potential deployment risks or integration bottlenecks early, and provide remediation strategies to de-risk customer outcomes. AI Orchestration & Workflow Automation: Leverage AI agents, Large Language Models (LLMs), and internal platforms to automate routine tasks such as RFP/Security Questionnaire responses, sandbox provisioning, basic telemetry monitoring, and internal/external documentation, reclaiming your time for strategic customer advisory. Education & Enablement: Partner closely with the Enablement Team to design, build, and govern technical resources. Define, document, and educate both internal teams and customers on deliverables such as Standard Operating Procedures (SOPs), architectural best practices, and technical guides to continuously raise the technical baseline across the organization. Minimum Qualifications Experience: Experience in a customer-facing technical role, such as Solutions Engineering, Customer Engineering, Professional Services,  Sales Engineering, Technical Account Management, or as a Forward Deployed Engineer (years of experience required will vary based on Associate, Senior, Staff, or Principal leveling). Architectural Mindset: Ability to white-board, design, and articulate complex enterprise network and security architectures clearly. Consultative & Advisory Skills: Excellent communication skills with the ability to translate deeply technical concepts into business outcomes for both hands-on engineers and C-level executives. Commercial Mindset: Proven track record of operating in a quota-carrying or revenue-aligned environment, with a strong understanding of SaaS metrics, business value translation, and enterprise sales cycles. Communication: Exceptional presentation and stakeholder management skills, capable of bridging the gap between deep technical implementation details and high-level C-suite business strategy. Technical Breadth: We do not expect every candidate to be an expert in every domain. Qualified candidates will demonstrate depth in at least four of the technical areas below: Internet fundamentals: Deep understanding of how the Internet works, including OSI Model layers 1–7, BGP, GRE, TCP/UDP, DNS (authoritative and recursive), HTTP(S), and SSL/TLS. Security: Expertise in the security of Internet properties, including WAF, Bot Management, Firewalls, Rate Limiting, (M)TLS, Zero Trust, and SASE concepts. Performance: Expertise in the performance of Internet properties, including speed, latency, caching, video streaming, HTTP/2, and TLS 1.3. AI Proficiency: Familiarity with prompt engineering, AI-assisted scripting and log analysis, LLM utilization, and workflow automation to augment everyday CE workflows. Coding/Scripting: Proficiency in Python, JavaScript, Bash, Lua, Go, or similar languages to write custom integration scripts, API connectors, and edge computing functions. Developer Platform Fluency: Experience designing, building, deploying or scaling applications using RESTful/GraphQL APIs, SDKs, webhooks, and developer tooling in cloud-native environments. Familiarity with infrastructure-as-code (Terraform, Pulumi), CI/CD pipelines, and observability for developer-led deployments. Strongly Preferred Qualifications (The "Good-to-Haves") Technical Enablement: Track record of creating and delivering technical enablement - workshops, code samples, documentation, or developer onboarding - that accelerates customer adoption of complex platforms. Applied AI Knowledge: Experience with LLM orchestration, Retrieval-Augmented Generation (RAG) architectures, and model-access controls. Regulatory Knowledge: Understanding of regulatory requirements such as PCI DSS, HIPAA, and SOC 2. Hyperscaler / Digital Native Background: Talent coming out of hyperscalers, digital natives, distributed systems environments, or startups is strongly valued for its deeper cloud fluency and advanced architecture depth. Education Undergraduate or graduate degree in Computer Science, Engineering, or a related field. Travel This role may require travel up to 20–50% to attend customer meetings, conferences, industry events, and in-person team collaboration. Compensation This role features a competitive base salary and a variable compensation structure tied directly to the successful execution of full-lifecycle commercial targets, including new business acquisition, ongoing adoption milestones, and overall account expansion. Compensation may be adjusted depending on work location. For Bay Area based hires: Estimated annual salary of $214,000.00 - $336,000.00 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Задачи: — Разрабатывать, обучать и внедрять ML/DL-модели для бизнес-задач. — Строить AI-агентов и multi-agent workflows с tool/function calling, MCP и LangGraph/LangChain (или аналогами). — Проектировать RAG-системы: эмбеддинги, hybrid search, reranking и векторные БД. — Выполнять fine-tuning LLM, оптимизировать inference и интегрировать модели через API и микросервисы. — Участвовать в MLOps/LLMOps: evaluation, мониторинг, tracing, guardrails и повышение качества AI-систем. Требования: — Опыт: от 2 лет в ML/DL или разработке AI-систем. — Стек: Python, NumPy/pandas, scikit-learn, PyTorch / TensorFlow, Hugging Face Transformers. — Знания: понимание ML/DL, архитектуры трансформеров и принципов работы LLM. — Навыки: практический опыт с AI-агентами, RAG, vector DB и structured output; уверенный Git и деплой в production. — Языки: знание узбекского и английского (Intermediate+) языков. Будет преимуществом: — Разработка API на FastAPI / Django REST Framework и контейнеризация с Docker. — Computer Vision и видеоаналитика (OpenCV, YOLO, tracking, RTSP). — MLOps/LLMOps-инструменты, self-hosted inference, GPU и Kubernetes. — Мультимодальные модели или голосовые пайплайны (STT-LLM-TTS). Условия: — Развитие: оплачиваемые профильные курсы, IT-конференции, фестивали и тимбилдинги. — Условия: бесплатные вкусные обеды в офисе. — Карьера: реальные масштабные задачи, влияние на продукты компании, профессиональный и финансовый рост. — Локация: офис в г. Ташкент. — График: 5/2, с 09:00 до 18:00. — Командировки: в г. Коканд (организуются и оплачиваются компанией). — Зарплата: От 14.000.000 сум.

Задачи: — Дизайн и построение LLM пайплайнов, AI агентов и RAG-систем с функциями вызова инструментов и структурированным выводом (JSON schema) — Разработка production-grade Python сервисов с использованием Django, Celery, Redis — Валидация продуктовых гипотез на основе реальных данных и аналитики — Измерение и оптимизация качества, производительности и стоимости LLM — Тесное сотрудничество с продакт-менеджерами и инженерами для превращения идей в масштабируемые решения Требования: — Опыт создания production LLM приложений — Практические знания RAG, embeddings, векторного поиска, prompt engineering и оценки LLM — Опыт работы с LangGraph, LangChain или аналогичными фреймворками — Сильный опыт Python backend (Django, PostgreSQL, асинхронное программирование) — Навыки работы с SQL и Pandas, ориентированность на данные — Владение английским языком на уровне B2-C1, русским на уровне C1 — Локация: любой город, кроме России и Беларуси Будет плюсом: — Опыт работы с векторными базами данных (Qdrant, pgvector и др.) — Опыт дообучения, классификации текста или работы с embedding моделями — Опыт с Kubernetes, gRPC или Go Условия: — Удалённая работа — Гибкий график — Профессиональное развитие Компания Mad Devs занимается решением сложных задач и созданием инновационных решений в области AI, проекты охватывают Европу, США и Юго-Восточную Азию. Вакансия направлена на революцию в юридической индустрии с помощью AI.

Location: London, United Kingdom - Deliveroo Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Senior Software Engineer, GenAI Platform About the Team Deliveroo's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalisation to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at Deliveroo and DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. Architect scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in. Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. Partner closely with — and raise the technical bar for — ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives. Set technical direction for the future of the company's centralised GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalisation. We’re excited about you because… BSc, MSc or PhD in Computer Science or equivalent 5+ years of industry experience in software engineering Deep backend engineering fundamentals, especially in Python and distributed systems. Track record of designing and owning production services, APIs, data pipelines, or ML infrastructure at scale. Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. Deep hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA). Demonstrated technical leadership: leading design across ambiguous, fast-moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Nice To Haves Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation GPU performance work — multi-node/distributed inference, KV-cache/memory optimisation, quantisation (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems Experience with LLM gateways, model routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self-serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

Location: Los Angeles, USA Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential, from secure and compliant systems to efficient business platforms that keep the company running smoothly. HR Tech & Systems builds and evolves the technology ecosystem that supports Rioters across the employee lifecycle. As a Staff AI & Data Engineer, HR Systems , you will bring deep AI and modern data engineering expertise to HR Systems, accelerating our evolution into an AI-augmented and data-enabled engineering organization. You will be a hands-on builder who designs and delivers AI agents, intelligent automations, data products, and reusable engineering capabilities across Riot's HR Technology ecosystem. You will combine strong AI and data engineering expertise with meaningful HR technology and domain knowledge. You will work across Workday, Databricks, and Riot's broader AI and enterprise technology ecosystem to turn trusted HR data and complex HR processes into secure, scalable solutions. Just as importantly, you will help build these capabilities within the existing team. You will pair with HR Tech engineers, establish practical engineering patterns, and provide hands-on mentorship in AI, Databricks, and modern development practices so the team can increasingly design, build, and support these solutions independently. You will partner closely with HR Tech & Systems, Enterprise Data & AI, Enterprise Software Engineering, HR COEs, and other Enterprise Technology teams. The role reports to the Senior Manager, HR Tech & Systems. Responsibilities: HR Technology Engineering & Automation Apply deep HR technology knowledge to discover opportunities and develop solutions across HR operations, recruiting, onboarding, compensation, benefits, learning, talent, workforce planning, and HR analytics. Leverage Riot's evolving AI and automation ecosystem, including tools such as Workato, Windsurf, Devin, and TrueFoundry, to accelerate development and delivery. Evaluate opportunities within HR Technology’s workflow and portfolio of applications, including SaaS and custom systems, to establish best practices and introduce new AI-enhanced capabilities. Partner with HR stakeholders, product owners, and technical teams to identify high-value opportunities and determine when AI, automation, data products, integrations, or native platform capabilities are the most appropriate solution. Translate complex HR processes, policies, and operational needs into scalable technical solutions that improve Rioter experiences and business outcomes. Drive solutions through the full lifecycle from discovery and prototyping through implementation, adoption, measurement, and continuous improvement. AI & Agent Engineering Design and build agentic AI solutions using LLMs, RAG, tool calling, memory, orchestration, and APIs to make business-context-aware decisions and take actions within defined guardrails. Leverage Databricks AI capabilities—including Unity Catalog, model serving, and vector search—and select appropriate models, prompting, retrieval, and orchestration patterns based on business needs, performance, scalability, and cost. Establish evaluation, testing, and observability practices that ensure AI solutions are accurate, grounded, reliable, safe, measurable, and production-ready. Design secure and responsible AI solutions with appropriate identity-aware access, human oversight, transparency, auditability, and controls for sensitive HR data and workflows. Partner with Enterprise Data & AI and Security to establish reusable AI patterns, standards, and guardrails, rapidly evaluating emerging capabilities and scaling those that demonstrate measurable business value. Data Engineering & Architecture Design scalable HR data pipelines, products, and domain models on Databricks using medallion architecture and Unity Catalog to provide governed, secure access to trusted HR data. Integrate structured and unstructured data across Workday, Databricks, enterprise applications, and AI services using MCPs, APIs, events, pipelines, and modern orchestration patterns. Establish data products, contracts, semantic models, and metadata that define ownership, business meaning, quality, lineage, freshness, and appropriate use of HR data. Ensure HR data is fit for analytics and AI through clear source-of-truth definitions, standardized business concepts, and strong data quality, privacy, security, and governance practices. Design resilient architectures that appropriately balance scalability, performance, reliability, and cost in partnership with Enterprise Data & AI and system owners. Team Enablement & Engineering Excellence Mentor and pair with HR Tech engineers to build AI agents, Databricks solutions, data products, and automations, growing sustainable AI and data engineering capability across the team. Establish reusable architectures, development patterns, documentation, and engineering standards that enable the team to independently build and scale solutions. Partner with the HR Tech & Systems Development Lead to establish AI/agent observability, reliability, adoption metrics, and SLA/SLO practices while optimizing architecture, model usage, and compute for performance and cost. Partner with technical leaders across Enterprise Technology to align HR Tech solutions with broader architecture and engineering standards. Required Qualifications: 7+ years of software engineering, data engineering, AI/ML engineering, solution engineering, or related technical experience, with demonstrated experience designing and delivering production solutions in enterprise environments. Demonstrated hands-on experience building and deploying generative AI and/or agentic AI solutions using LLMs, RAG, tool calling, orchestration, APIs, and modern AI architectures. Demonstrated experience designing and delivering secure, resilient enterprise integrations within event-driven architectures using MCPs, APIs, webhooks, orchestration platforms, AWS lambda and relevant SaaS capabilities, with robust authentication, authorization, idempotency, retry, and error-handling patterns. Hands-on experience with Databricks, including Unity Catalog, medallion architecture, and AI capabilities such as model serving, vector search, or Mosaic AI. Experience designing scalable data pipelines, data models, and data products using structured and unstructured enterprise data. Meaningful hands-on experience with HR technology, HR data, and employee lifecycle processes, including enterprise platforms such as Workday, VNDLY, Greenhouse, or ServiceNow. Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data/AI, or a related field, or equivalent practical experience. Desired Qualifications: Deep hands-on experience with Workday, including integrations/APIs, Extend, Prism, reporting, Business Process Framework, or Workday data architecture. Experience with MCP, multi-agent architectures, enterprise knowledge systems, semantic search, and advanced RAG architectures. Experience with Workato, Windsurf, Devin, TrueFoundry, Microsoft Copilot, or comparable AI development, automation, and deployment platforms. Experience with AWS, GCP, or another major cloud platform, including CI/CD, DevOps, and production observability. Experience with HR Analytics, workforce planning, talent intelligence, or other HR domain data models. For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes your fellow Rioters, who are the customers of your work. Being a dedicated fan of games is not necessary for this position! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Задачи: — Проведение пентестов web-приложений и API (black-box, grey-box, white-box), включая REST, GraphQL, gRPC и WebSocket. — Тестирование безопасности AI/LLM-функциональности (Prompt Injection, Data Leakage, RAG, MCP, Agentic AI и другие сценарии по OWASP Top 10 for LLM). — Проверка механизмов аутентификации и авторизации (OAuth 2.0, OIDC, SAML, JWT, SSO, MFA), а также контроля доступа и поиска ошибок (IDOR/BOLA, эскалация привилегий, multi-tenancy). — Поиск и эксплуатация технических и бизнес-логических уязвимостей, построение сложных цепочек атак (Attack Paths). — Анализ безопасности клиентской и серверной части приложений, зависимостей, Software Supply Chain и исходного кода. — Разработка собственного offensive-инструментария, PoC, расширений Burp Suite и средств автоматизации на Python/JavaScript. — Подготовка технических отчетов с описанием рисков, рекомендациями по устранению и проведение ретестов после исправлений. — Участие в Threat Modeling, Security Architecture Review и, при желании, в активностях Purple Team. Требования: — Опыт проведения ручного пентеста web-приложений и API от 4–5 лет, уверенное владение методиками black-box, grey-box и white-box. — Глубокое понимание современных web-уязвимостей (OWASP Top 10) и практический опыт эксплуатации XSS, SSRF, SSTI, SQL/NoSQL Injection, XXE, Deserialization, CSRF, CORS, Prototype Pollution, Path Traversal и других. — Опыт тестирования REST, GraphQL, gRPC, WebSocket API, поиска бизнес-логических уязвимостей и обхода механизмов защиты приложений. — Практический опыт анализа аутентификации и авторизации (OAuth 2.0, OIDC, SAML, JWT, SSO, MFA), включая поиск ошибок реализации. — Экспертное владение Burp Suite Professional, опыт разработки собственных расширений будет преимуществом. — Уверенное знание Python и JavaScript/TypeScript для разработки PoC, эксплойтов и автоматизации тестирования. — Опыт проведения Security Research, анализа современных фреймворков и архитектур (React, Angular, Vue, Spring, .NET, Node.js, Go и др.). — Будет плюсом опыт тестирования AI/LLM-приложений, участия в Threat Modeling, Security Architecture Review или Purple Team. Условия: — Удаленная работа. — Опыт более 6 лет. — Зарплата обсуждается на собеседовании.

Задачи: — Разработка AI-агентов, которые выполняют рабочие процессы, а не только чат-интерфейсы. — Работа с агентами, которые подключаются к API и внутренним системам, используют инструменты, извлекают контекст, принимают решения на нескольких шагах и восстанавливаются при сбоях. — Полный цикл разработки: от проектирования агентов до их оценки, развертывания и обеспечения надежности в продакшене. Требования: — Опыт разработки на Python и бекенд инженерии. — Опыт работы с LLM API и агентными системами в продакшене. — Знание LangGraph, LangChain, LlamaIndex или аналогичных технологий. — Опыт работы с вызовом функций/инструментов и структурированными выводами. — Знание RAG, embeddings, векторного поиска и управления контекстом. — Опыт работы с состоянием агента, памятью, маршрутизацией, повторными попытками и логикой резервного копирования. — Интеграция API и событийно-ориентированные рабочие процессы. — Опыт оценки, трассировки и отладки LLM. — Навыки работы с Docker, Kubernetes, CI/CD и AWS. — Приветствуется опыт мультиагентной оркестрации, MCP, workflows с участием человека и мониторинга агентов в продакшене. Условия: — Удаленная работа. — Полная занятость или контракт.

Задачи: — Разработка AI-агентов, которые выполняют рабочие процессы, а не только чат-интерфейсы. — Работа с агентами, которые подключаются к API и внутренним системам, используют инструменты, извлекают контекст, принимают решения на нескольких шагах и восстанавливаются при сбоях. — Полный цикл разработки: от проектирования агентов до их оценки, развертывания и обеспечения надежности в продакшене. Требования: — Опыт разработки на Python и бекенд инженерии. — Опыт работы с LLM API и агентными системами в продакшене. — Знание LangGraph, LangChain, LlamaIndex или аналогичных технологий. — Опыт работы с вызовом функций/инструментов и структурированными выводами. — Знание RAG, embeddings, векторного поиска и управления контекстом. — Опыт работы с состоянием агента, памятью, маршрутизацией, повторными попытками и логикой резервного копирования. — Интеграция API и событийно-ориентированные рабочие процессы. — Опыт оценки, трассировки и отладки LLM. — Навыки работы с Docker, Kubernetes, CI/CD и AWS. — Приветствуется опыт мультиагентной оркестрации, MCP, workflows с участием человека и мониторинга агентов в продакшене. Условия: — Удаленная работа. — Полная занятость или контракт.

Вакансия бизнес-аналитика в команду аналитиков, которая работает с AI-проектами, включая LLM, ML, Computer Vision, RAG и другие сценарии автоматизации для бизнеса.