Senior AI Engineer - Agentic AI
Actively Hiring
Full-time Posted 19 days ago
Responsibilities
- check_circle Design, build and deploy Generative AI and Agentic AI solutions from prototype to production
- check_circle Implement multi-agent orchestration patterns using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel or OpenAI Agents SDK
- check_circle Develop the orchestration backbone for advanced workflows including planning, checkpointing, retries, fallback handling and resumption of long-running processes
- check_circle Build and optimize RAG pipelines, including chunking strategies, embeddings, vector/hybrid search and retrieval evaluation with grounded responses and citations
- check_circle Develop memory and context management solutions, including short-term and long-term stores and compaction strategies
- check_circle Write robust Python APIs and services (e.g., FastAPI), incorporating async execution, background jobs and containerized deployments
- check_circle Integrate enterprise systems and tools using protocols such as MCP, A2A, OpenAPI, REST and gRPC, ensuring graceful degradation and retries
- check_circle Apply enterprise security and governance practices including RBAC, prompt safety checks, traceability and secrets management
- check_circle Implement evaluation pipelines and observability frameworks using tools such as Langfuse, Arize or OpenTelemetry
- check_circle Contribute to architectural design decisions, code reviews and engineering standards for platform development
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering or related field (PhD is a plus)
- Practical experience delivering Generative AI or Agentic AI systems into production environments
- Expertise in Python engineering for APIs, microservices, testing and CI/CD workflows
- Strong working knowledge of LLM capabilities, including prompt design, structured outputs, tool calling and retrieval strategies
- Hands-on experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI or Semantic Kernel)
- Proven experience with RAG implementations, embeddings and vector database integrations
- Familiarity with stateful or long-running systems, including checkpointing and resumable workflows
- Cloud deployment experience (Azure preferred), using services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes
- Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow
- Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences
Preferred qualifications
- Experience using Azure AI Foundry or Microsoft Agent Framework
- Knowledge of MCP and A2A protocols for agent and tool interoperability
- Hands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvector
- Familiarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architectures
- Experience with open-source LLMs or Small Language Models for custom deployments
- Knowledge of AI safety and governance: guardrails, output filtering and red-teaming practices
- Background in fine-tuning or adapting foundation models (e.g., LoRA, distillation) for domain-specific tasks
- EPAM Employee Stock Purchase Plan (ESPP)
- Protection benefits including life assurance, income protection and critical illness cover
- Private medical insurance and dental care
- Employee Assistance Program
- Competitive group pension plan
- Cyclescheme, Techscheme and season ticket loans
- Various perks such as free Wednesday lunch in-office, on-site massages and regular social events
- Learning and development opportunities including in-house training and coaching, professional certifications, and courses
- If otherwise eligible, participation in the discretionary annual bonus program
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
Tags & Focus Areas
Ai Ai Engineer Generative Ai
About EPAM Systems
Shape the future of global data science infrastructure as a Site Reliability Engineer at EPAM. You'll architect, implement, and support cutting-edge platforms like Alteryx, Dataiku, and Azure Machine Learning that power critical business insights across wealth management, investment banking, and corporate functions.
Ready to Join the Team?
Apply once with DevFound — we route your profile to EPAM Systems and keep you posted on matching AI roles.