Вакансии

- разработка новой функциональности ПО для создаваемых компанией устройств на архитектуре AArch64,. - устранение актуальных проблем ПО.

— Анализировать бизнес-процессы и требования заказчиков; — Проводить обследование существующих решений и процессов;

Рефакторинг и оптимизация существующей кодовой базы. Разработка и интеграция со сторонними API.

Писать сложные SQL-запросы для анализа и валидации данных: использовать CTE, оконные функции, сложные JOIN, создавать и изменять объекты базы данных. Разрабатывать хранимые процедуры и настраивать их регулярное выполнение.

Подготовка и настойка тестового окружения (Linux, Windows, MacOS, iOS, Android). Выполнение ручного тестирования продуктов (десктопные и мобильные приложения, утилиты, плагин для web-браузеров).

Разрабатывать и поддерживать Ansible playbook’и, шаблоны конфигураций и docker-compose файлы. Внедрять практики IaC для управления инфраструктурой.

разработка тестовых сценариев, подготовка данных и проведение тестирования (функционального, интеграционного, регрессионного). создание и поддержка тестовой документации (чек-листы, тест-кейсы, отчеты).

О компании ООО «ИНГРАД Проект» — проектная организация полного цикла, с 2012 года создающая архитектурные и инженерные решения для премиальных жилых комплексов бизнес-класса в Москве. Среди заказчиков — MR Group, ФСК, Pioneer, Гранель. Компания — лауреат и дипломант конкурса «Зодчество», обладает статусом BIM-лидера России, BIM-отдел насчитывает 25 специалистов. Задачи: — Самостоятельная разработка frontend с проектированием frontend system design: продумывать структуру компонентов, модель состояния, работу с API и масштабируемость решений до начала разработки. — Полное ведение функциональности от идеи до merge. Требования: — Уверенное владение TypeScript/JavaScript, React, React Router. — Опыт работы с TanStack Query/React Query, React Hook Form. — Знание REST API, HTML/CSS, адаптивной верстки. — Владение Git, понимание SPA, управление client/server state. — Опыт с авторизацией и правами доступа. — Навыки написания unit, integration, e2e тестов. — Понимание производительности frontend-приложений, SSR. Будет плюсом: — Знание MUI, Tailwind CSS, GraphQL/Apollo, Vite/Webpack, Jest/Vitest, React Testing Library, Cypress/Playwright, Storybook, GitHub Actions, Docker, WebSocket/SSE. Условия: — Гибкое начало дня, возможность удалённого или гибридного формата работы. — Компенсация фитнеса. — Возможность работать из «южного офиса» у моря. — Доступ к корпоративным поездкам в Шерегеш и другим мероприятиям команды.

Location: Remote-Friendly (Travel Required) | San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Claude is trained and served on hardware in data centers Anthropic operates and in sites partners build and run for us. DCSE secures that footprint: the sites, the hardware moving through them, and the people and vendors who touch it. Security at that scale runs on data, and you'll be the engineer who owns it and the systems that produce it: component-level hardware inventory and chain of custody, fed by software that runs at our sites and on hosts, and continuous control monitoring, with the posture calculations that decide what each site is cleared to run. You set the architecture, agree schemas and interfaces with the teams, operators and vendors that own the sources, build the pipelines, stores and tools, and run what you ship. The goal is security that runs itself: custody, access and evidence verified continuously from records, with tools and AI agents doing the reviews. We favor simple, standard controls and tools that can be verified over elaborate ones. We use Claude throughout our engineering workflow and expect you to as well. You'll work with the rest of DCSE, including the engineers on its security monitoring platform, with Anthropic's compute and data center operations teams, and directly with data center operators, hardware vendors and logistics providers. The role is based in San Francisco, with remote applicants in the US considered, and involves regular travel to data center sites, which are critical infrastructure. Key responsibilities Hardware inventory and chain of custody: own the component-level record of what hardware exists, where it is and who has handled it: the data model, the pipelines that join inventory and custody events from many source systems, change history on every record, and the tamper-evident stores behind it Tools, automation and AI agents: build the tools and agents the team runs on this data: reconciliation, audit evidence exports, custody checks. When a review keeps costing people time, you build the tool or agent that retires it Custody workflows and field software: build the workflows and the site and host software that capture custody events as they happen (a receipt, a move, a data wipe, a decommission) and enforce who is authorized to record them Continuous control monitoring and posture: own the pipelines that turn check outcomes, assessment results and findings into per-control and per-site measures over time, against the compliance frameworks we and our partners are assessed against, and the posture calculation that decides what a site is cleared to run Data quality: build coverage checks, reconciliation against sources and alerts on a silent or drifting feed, so a wrong or missing record is caught the same day; own retention and access controls on the records Integration and reporting: agree schemas and interfaces with the engineers who run the security monitoring platform, and build the query surfaces and reports the team, site leads and leadership use directly Minimum qualifications Have designed and run production data systems (relational schemas, change data capture, batch or streaming pipelines and the services around them) and kept them correct: coverage measured, records reconciled against the source, alerts on silence, and a design that expects records to get corrupted or lost Have integrated with systems you did not control (inventory tools, ticketing systems, vendor APIs) and built data models that outlive any one source Have shipped software that runs outside a central service (host agents, site-local services, edge collectors) and kept it correct in the field, or built metrics a team made decisions on and defended them when challenged Have worked hands-on with data about physical assets or security controls: a hardware fleet, data center or supply chain inventory, asset custody, or the control and evidence data of a security or compliance program Read and review other people's code, and build with AI coding agents using Git and code review Own a domain end to end: set the approach, design and build it with the teams and vendors whose systems it depends on, and run it as something people use every day Write SQL and at least one general-purpose programming language well Preferred qualifications Experience with asset management, supply chain or warehouse systems where serialized tracking and custody transfer are the core problem Experience building continuous control monitoring or metrics for a security or compliance program, or producing evidence an assessor accepted, for example against NIST SP 800-53, ISO 27001 or SOC 2 Experience with risk or posture scoring that combines control results, findings and other evidence into a measure people make decisions on Familiarity with data center hardware and its lifecycle from receipt to decommission, and with sites and parties you did not control: colocation, cloud or GPU cloud operators, hardware manufacturers, logistics providers Have run software on many hosts or at remote sites with versioned rollout and a way to know each copy is still reporting Have built AI agents or automation that replaced recurring manual work The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

We're looking for talented technical artists who enjoy solving creative problems, building polished gameplay experiences, and working closely with designers, programmers, and artists. • Minimum 2 years of professional experience as a lighting artist (4+ years of experience is preferred).

– опыт работы с 1С:ЗУП от 5 лет; – обязателен опыт работы в EDT / Git.

Location: Limassol, Cyprus Spotware Systems is looking for a Senior Technical Writer to join our Content & Education Team. We are seeking an experienced technical writer who combines excellent documentation skills with hands-on expertise in AI-driven production, automation and publishing. You will help build scalable workflows that deliver accurate, consistent documentation for complex financial technology products. Responsibilities : Create and maintain clear technical documentation, including user guides, illustrated how-tos and integration manuals. Design and improve AI-assisted documentation workflows, from source research and drafting to review, validation, localisation readiness and delivery. Structure and optimise documentation for AI readability, search visibility and reliable retrieval by agents. Use AI tools and agents to automate repetitive documentation tasks, while verifying technical accuracy against authoritative product sources. Maintain and enhance documentation portals (Docusaurus, MkDocs or similar platforms) and manage content through docs-as-code, Git and review workflows. Collaborate with product, engineering and other stakeholders to turn releases and new features into accurate, timely documentation. Produce release notes and selected B2B/B2C materials, including brochures, white papers and product-video scripts. Establish and apply terminology, style, accessibility and quality standards, including automated checks for accuracy, terminology, style and links. Requirements : Proven experience writing technical documentation for trading, forex, financial technology or similarly complex software products, backed with a strong portfolio. Strong English writing, editing and instructional-design skills. Practical experience with documentation platforms, Markdown, Git and docs-as-code workflows. Hands-on experience using AI assistants, agents, MCP servers, APIs or similar tools into repeatable documentation production and quality-assurance workflows, beyond prompting alone. Ability to evaluate AI-generated output critically, validate claims against product evidence and protect sensitive information. Ability to work independently across teams, prioritise delivery, respond to feedback and improve processes continuously. Conditions : Competitive remuneration reflecting your skills, contribution, and experience. Comprehensive private medical insurance for your peace of mind and well-being. Relocation support package (if applicable) — including visa and work permit arrangements to ensure a smooth move to Cyprus. Welcome Baby Bonus to celebrate life’s most joyful milestones. Birthday Voucher to make your special day even brighter. Fully equipped, modern workplace offering daily fresh fruits, Wednesday breakfasts, Thursday pizza days, and Friday snacks — because great work deserves great food. Sports and wellness benefits, including company-subsidized gym memberships and wellness programs to help you stay active and energized. Support with integration into life in Cyprus, including guidance on local healthcare, banking, and accommodation. Continuous learning and professional growth opportunities, including internal training, conferences, and skill-development initiatives. Greek language classes to help you adapt smoothly to local life and culture.

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 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 3+ 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 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 3+ 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 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 3+ 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: Limassol, Cyprus 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 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 3+ 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 Help us challenge injustice by creating fair choices for millions of people across in 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. Annual salary starting from 59 200 EUR gross Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid inDrive is a global technology company building products used by millions of people around the world. We are looking for a Senior DevOps Engineer to join our Market Intelligence team and help build and maintain reliable, scalable, and efficient infrastructure for data-driven products. In this role, you will work closely with development and data teams, improve CI/CD processes, automate infrastructure and deployments, and ensure the stability and scalability of our systems across different environments Key Responsibilities Building a geo-distributed infrastructure, ensuring maximum performance and reliability of our services Participation in the life cycle of projects at all stages, including the design and development of services, resource planning and maintenance Improving processes related to system maintenance (fault tolerance, scaling, postmortems, finding and eliminating bottlenecks) Automation of build, test and application delivery processes Development of services and infrastructure management tools Taking part in creation of PaaS platform On-call duty Skills, Knowledge and Expertise Deep knowledge and understanding of Linux OS (Ubuntu, Amazon Linux) Deep knowledge of Docker / Containerd containerization technologies Experience in building CI/CD Pipelines Experience in using Ansible / Saltstack and writing roles / playbooks / states / pillars Strong understanding of command of Git Ability to independently decompose tasks and bring them to the end 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. Annual salary starting from 59 200 EUR gross. Final benefits may vary depending on the location.

– Опыт коммерческой разработки от 5 лет и уверенное владение Python. – Умение проектировать и сопровождать production-сервисы: API, модели данных, очереди и фоновые задачи, тесты, метрики, логи и tracing.