Senior AI/DevOps Engineer (Azure)

Quick Facts

• Role: Senior AI/DevOps Engineer (Azure)

• Focus: Building and operating Azure infrastructure and pipelines for LLM/RAG AI features

• Work style: Remote-first with occasional in-office days (Gdańsk)

Description

You will sit at the intersection of infrastructure, automation, and applied AI—building the pipelines, platforms, and production guardrails that let the team ship, scale, and keep AI-powered features healthy in production. The role includes evening overlap with US teams and requires comfort working asynchronously across time zones.

Responsibilities

• Design and operate infrastructure powering LLM integrations, RAG pipelines, vector stores, and model-serving endpoints

• Build and maintain CI/CD pipelines for traditional services and AI/ML workloads (deployment, evaluation, rollback)

• Own observability and monitoring for AI systems (latency, cost, token usage, output quality drift, failure modes)

• Architect and manage Azure infrastructure (compute, networking, storage, IAM) using infrastructure-as-code

• Containerize and orchestrate services with Docker and Kubernetes

• Integrate with AI APIs/platforms and build internal tooling to make integrations repeatable across teams

• Implement guardrails such as rate limiting, caching, and cost controls for LLM-powered features

• Provide light ongoing support for legacy .NET Framework 4.8 applications and support modernization/migration planning

• Collaborate with product, data, and application engineering teams to deliver reliable, production-grade systems

• Mentor other engineers and help set technical direction for AI infrastructure practices

Requirements

• 5+ years of professional experience in DevOps, platform engineering, or SRE roles

• Hands-on experience deploying and operating systems in production

• Strong experience with Azure cloud infrastructure and infrastructure-as-code (Terraform, Bicep, ARM, or similar)

• Solid grasp of CI/CD pipeline design and automation (Azure DevOps, GitHub Actions, or similar)

• Proficiency with Docker and Kubernetes for containerization and orchestration

• Experience integrating with AI/LLM APIs and understanding of RAG architectures, vector databases, and prompt/response evaluation

• Comfort with monitoring and observability tooling (Prometheus, Grafana, Application Insights, or similar), including AI-specific metrics such as cost, latency, and quality

• Working knowledge of .NET/C# (including .NET Framework 4.8 to support and troubleshoot legacy systems)

• Comfortable working independently and owning problems end-to-end

Benefits

• Competitive compensation based on skills and experience

• Regular performance appraisals

• Flexible workspace (remote or office)

• Bonuses for article writing and public talks

• Generous time off (vacation, national holidays, sick leave, family days)

• Personalized learning programs, plus free tech webinars and meetups

• Generous corporate online activities and supportive community