Senior AI Engineer

We are seeking a Senior AI Engineer to build a central GenAI Platform, enabling rapid development, validation, and deployment of AI agents for hundreds of product teams across the organization. In this role, you will design and deliver full-stack systems, shape platform capabilities, and help accelerate the impact of AI agents across multiple domains. This position is based in Lithuania, with relocation support available from EPAM. Learn more about the benefits EPAM Lithuania provides in the description below. Responsibilities Design and implement full-stack applications, AI agents and platform components that enable rapid GenAI agent development, validation and deployment Build developer tooling, CI/CD and observability for safe, fast iteration (evals, canaries, rollout/rollback, cost and quality telemetry) Apply secure SDLC and privacy-by-design practices (threat modeling, least privilege) Collaborate with product, UX and domain experts to deliver customer-focused solutions with measurable outcomes Apply current LLM patterns (e.g., RAG, retrieval, routing, tool-use, evaluation) to deliver measurable customer value-faster, more reliable AI systems; reduced time-to-decision; improved trust/safety metrics; and lower cost per query Lead by example through high-quality, maintainable code Requirements 5+ years of professional software engineering experience Strong full-stack development skills, including hands-on experience with TypeScript and major cloud platforms (AWS, Azure, Google Cloud Platform) Expertise in at least one, and proficiency in several of the following: AI Agent development and evaluation (including LLM platforms and frameworks such as LangChain), Backend (Python, FastAPI, TypeScript) and frontend (TypeScript, Streamlit, Gradio) development, Cloud platforms and services, CI/CD, Infrastructure as Code, Site Reliability Engineering, quality engineering and secure SDLC Proven track record of secure, reliable and scalable cloud-native systems Experience with AI solutions at scale, using advanced integration patterns (e.g., RAG, Agents) and frameworks such as LangChain Experience with vector databases (e.g., Qdrant, FAISS, Chroma) Strong problem-solving, ownership and cross-functional communication skills Nice to have Experience with experiments, A/B tests and model iteration based on user feedback Understanding of retrieval systems (keyword search, vector search, embeddings) and ranking algorithms Familiarity with emerging AI protocols (e.g., MCP, A2A, ACP) Experience with cloud AI platforms (Azure OpenAI, Amazon Bedrock, Google Cloud Platform Vertex AI) or on-premise solutions (e.g., vLLM) Experience with enterprise AI platforms (AWS AgentCore, Databricks AgentBricks, Google Agents Space, Azure AI Foundry) Experience with observability and monitoring tools/frameworks Technologies Python, TypeScript, PyTorch, Hugging Face, LangChain Vector databases (Qdrant, FAISS, Chroma) LLM APIs (Azure OpenAI, AWS Bedrock)