[Hiring] Lead Full-Stack, Data and AI Agent Engineer @EPAM

Role Description

We are looking for a Lead Full-Stack, Data and AI Agent Engineer to build a production-grade agentic AI platform for upstream oil & gas operations (ESP, gas lift, plunger lift, chemical injection). As a Lead Full-Stack, Data and AI Agent Engineer, you will evolve the deep-agent core that converts petroleum engineers' natural-language questions into safe, schema-aware SQL and multi-step reasoning workflows, and help the team ship reliably.

• Lead ownership of the deepagents/LangGraph agent runtime, extending agent tools, subagents, and safety middleware.
• Design FastAPI endpoints using async I/O (asyncpg/psycopg3) and SSE streaming to deliver real-time agent responses.
• Implement SELECT-only, injection-safe SQL generation across per-tenant PostgreSQL schemas.
• Integrate AWS Bedrock (Claude via langchain-aws) and manage LLM factory/model routing decisions.
• Instrument agent runs with MLflow and contribute improvements to the eval framework.
• Collaborate on RAG retrieval using pgvector, FAISS, and S3-backed document storage.

Qualifications

• Proven track record with 5+ years of backend engineering in Python, including strong async/await skills and FastAPI or a comparable framework.
• Hands-on experience with AWS core services such as Bedrock, RDS, and S3, following a strict no-hardcoded-credentials approach.
• Practical experience using LangGraph, LangChain, or a similar agent-orchestration framework.
• Solid SQL/PostgreSQL experience, with confidence owning safety-critical code such as SQL injection prevention and tenant isolation.
• Deep understanding of LLM APIs (Bedrock, OpenAI, Anthropic) and prompt engineering techniques.
• Strong CI/CD and testing discipline, including lint/format gates, pytest/Jest, and GitHub Actions.
• Security-first mindset for tenant isolation, including tenant isolation, SELECT-only SQL, and no cross-tenant leakage.
• Observability and eval mindset using MLflow or equivalent, measuring quality, latency, and tokens before shipping.
• Ability to learn oil & gas / production-ops terminology (ESP, gas lift, decline curves) even without prior background.
• English proficiency for client-facing communication; Spanish is a nice-to-have.

Requirements

• Working knowledge of TypeScript/React/Next.js, including SSE event formats, content types, and payload shapes for cross-stack debugging.
• Familiarity with uv, Ruff, and pytest workflows.
• Experience with MLflow.
• Familiarity with Redis/APScheduler.
• Background in oil & gas domain knowledge.