Python AI Engineer

EPAM Systems; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

EPAM Systems Inc is seeking a Python AI Engineer to evolve an enterprise knowledge base platform from Confluence-focused RAG to a broader knowledge system. You will build robust backend services, design retrieval and agent runtimes, and improve grounding and observability across the stack.

You will work on hybrid retrieval, multi-source ingestion, and production-grade AI infrastructure with a strong focus on reliability and scalable architectures.

Qualifications

• Proven hands-on Python experience in platform or infrastructure-heavy environments.
• Experience building CLI tools using Python, Golang, or Rust.
• Hands-on experience with LangGraph, LangChain, pgvector, and modern retrieval pipelines.
• Experience designing evaluation frameworks for LLM-backed systems and quality measurement.
• Strong experience with Docker, Helm, GitHub Actions, and Kubernetes-centric workflows.
• Familiarity with embedding pipelines, vector search, and LLM-backed systems.

Responsibilities

• Architect and implement robust backend services using Python 3.11, FastAPI, Pydantic, SQLAlchemy async, and asyncpg.
• Design trade-offs around retrieval quality, latency, cost, safety, and operational simplicity.
• Build production-grade agent runtime capabilities including memory boundaries and tool sandboxing.
• Improve grounding, failure analysis, and citation enforcement for dependability.
• Create observability and feedback loops using OpenTelemetry, Prometheus/Grafana, Docker/Helm, GitHub Actions.
• Oversee ingestion across current and future content sources and ensure end-to-end observability.

Skills

Python programming

CLI tool development

Retrieval pipeline design

LLM-backed systems

Observability & metrics

System reliability

Tools

Docker

Kubernetes

GitHub Actions

Helm

LangChain

LangGraph

pgvector

OpenTelemetry

Prometheus

Job description

We are hiring a Python AI Engineer to operate and evolve the infrastructure behind an enterprise knowledge base platform as it transitions from a Confluence-focused RAG chatbot into a broader, agentic knowledge system. Today, our platform supports Confluence/GitHub ingestion → chunking → pgvector → RAG retrieval → FastAPI serving. In our next growth phase, we are expanding toward hybrid retrieval (vector + sparse + graph), multi-source ingestion, robust evaluation pipelines, agent infrastructure, and shared chat platform primitives. If you are passionate about building scalable, reliable, and production-grade AI infrastructure rather than just chasing demo behavior, we want to hear from you.

Req.#1093687315

Responsibilities

• Architect and implement robust backend services using Python 3.11, FastAPI, Pydantic, SQLAlchemy async, and asyncpg
• Design trade-offs around retrieval quality, latency, cost, safety, and operational simplicity
• Build production-grade agent runtime capabilities, including memory boundaries, tool sandboxing, permissions, and budget controls
• Improve answer grounding, failure analysis, and citation enforcement for enterprise‑grade dependability
• Create comprehensive observability and operational feedback loops using OpenTelemetry, Prometheus/Grafana, Docker/Helm, and GitHub Actions
• Ingestion infrastructure across current and future content sources
• Full-stack observability across application, pipeline, database, and model‑serving behavior
• Cost, latency, throughput, and failure‑mode management for AI‑heavy workloads
• Release workflows that validate AI behavior changes, not just code compilation
• Partner closely with product and engineering teams to support multiple conversational surfaces through a unified knowledge platform

Requirements

• Strong, hands‑on Python experience in platform, automation, or infrastructure‑heavy environments
• Experience building CLI tools using Python, Golang, or Rust
• Hands‑on experience with LangGraph, LangChain, pgvector, and modern retrieval pipelines
• Experience designing evaluation frameworks for LLM‑backed systems, including regression detection and quality measurement
• Strong experience with Docker, Helm, GitHub Actions, and Kubernetes‑oriented workflows
• Systems Understanding: Familiarity with the operational characteristics of embedding pipelines, vector search, and LLM‑backed systems
• Strong skills across metrics, tracing, dashboards, alerting, and log analysis
• Experience with ingestion, ETL, or content‑processing pipelines at scale
• An innate ability to think in terms of reliability, cost, latency, throughput, and disaster recovery
• Nice to have Experience with Qdrant, Neo4j, or other vector/graph infrastructure
• Prior experience supporting RAG, search, evaluation, or agent platforms
• Background in enterprise or highly regulated environments
• Familiarity with enterprise tooling such as Vault, Splunk, Artifactory, or ECR
• Comfort leveraging AI‑assisted engineering workflows in day‑to‑day work