We are building a Chief Python Developer role to set technical direction and lead hands-on delivery for backend AI services and LLM orchestration. You will write production Python, mentor engineers, and collaborate with Security, Data, and Infrastructure while aligning with US-based product and engineering partners. Help raise observability, reliability, and operational excellence across pod services—apply now.
Responsibilities
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Define technical direction for the pod and own architecture decisions within scope
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Deliver production code (~50%+ of the time) across backend AI services, LLM orchestration and frontend integration
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Design and build Agentic Experiences (AX) with streaming, low-latency agent UIs
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Coach 3 Senior Engineers and 1 Data Engineer within the pod
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Collaborate daily with Security, Data and Infrastructure teams
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Coordinate with US-based product and engineering counterparts
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Run design reviews and remove blockers to protect sprint deliverables
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Uphold observability, SLOs and reliability standards for pod services
Requirements
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Proven track record with 7+ years of software engineering experience owning complex backend systems end-to-end
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Hands-on experience of 3+ years with LLM-era AI/ML platforms such as LangChain, LangGraph or Bedrock in production; deeper pre-LLM ML backgrounds (TensorFlow, scikit-learn) with a clear recent pivot to generative AI are also considered
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Expert-level proficiency in Python for backend services and AI integration as the primary language for backend and AI work
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Deep expertise in LangChain, LangGraph and LangSmith to orchestrate multi-step, multi-agent workflows plus evaluation/observability
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Solid background running production workloads with AWS, Docker and microservices
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Practical skills with GitHub Actions, ArgoCD and OpenTofu/Terraform
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Strong understanding of secure coding practices, auth/authz awareness and data governance as default standards
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Advanced knowledge of API design including RESTful APIs and microservices
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English proficiency at B2 (Upper-Intermediate) level or higher
Nice to have
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Fluency in React and TypeScript with ability to contribute to UI-layer code and review frontend work
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Familiarity with MCP (Model Context Protocol) and emerging agent interoperability standards
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Knowledge of AI evaluation tooling such as RAGAS or custom eval frameworks
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Background designing and optimizing RAG pipelines with vector databases (Amazon Kendra, OpenSearch)
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Understanding of identity/security domains (IAM, CIAM) and working with unstructured data (images, videos) using prompt context management via LangGraph State
We offer
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International projects with top brands
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Work with global teams of highly skilled, diverse peers
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Healthcare benefits
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Employee financial programs
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Paid time off and sick leave
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Upskilling, reskilling and certification courses
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Unlimited access to the LinkedIn Learning library and 22,000+ courses
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Global career opportunities
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Volunteer and community involvement opportunities
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EPAM Employee Groups
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Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn
