Lead Full-Stack, Data and AI Agent Engineer

EPAM Systems Inc; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

EPAM Systems seeks a Lead Full-Stack, Data and AI Agent Engineer to build a production-grade agentic AI platform for upstream oil & gas operations. You will evolve the deep-agent core translating engineers’ natural-language questions into safe, schema-aware SQL and multi-step reasoning workflows, helping the team ship reliably.

You will own the LangGraph runtime, design FastAPI endpoints with async I/O and SSE, ensure injection-safe SQL across tenants, integrate Bedrock, and measure quality with

Qualifications

• 5+ years backend experience in Python with async concepts.
• Hands-on with AWS Bedrock, RDS, S3 and no hard-coded credentials.
• Experience with LangGraph/LangChain.
• Strong SQL/PostgreSQL with safety for multi-tenant isolation.
• Familiar with LLM APIs (Bedrock, OpenAI, Anthropic) and prompt engineering.
• CI/CD and testing discipline (lint, pytest/Jest, GitHub Actions).
• Security-first mindset for tenant isolation and injection prevention.
• Observability mindset using MLflow or equivalents.
• Willingness to learn oil & gas terminology.

Responsibilities

• 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

Skills

Python

Async/await

FastAPI

SQL/PostgreSQL

LLM APIs

CI/CD

Security

Observability

English

Oil & gas domain

Team leadership

Spanish

TypeScript/React

Testing (pytest/Jest)

GitHub Actions

Redis

Apscheduler

MLflow

Tools

FastAPI

LangGraph

LangChain

PostgreSQL

AWS Bedrock

S3

Psycopg3

MLflow

Redis

pytest

Jest

GitHub Actions

uv

Job 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.

Responsibilities

• 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

Requirements

• 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
• Nice to have
• 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