Python AI Engineer

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

Benefits

• Medical, Dental and Vision Insurance (Subsidized)
• Health Savings Account
• Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
• Short-Term and Long-Term Disability (Company Provided)
• Life and AD&D Insurance (Company Provided)
• Employee Assistance Program
• Unlimited access to LinkedIn learning solutions
• Matched 401(k) Retirement Savings Plan
• Paid Time Off – the employee will be eligible to accrue 15-25 paid days, depending on specific level and tenure with EPAM (accrual eligibility may change over time)
• Paid Holidays - nine (9) total per year
• Legal Plan and Identity Theft Protection
• Accident Insurance
• Employee Discounts
• Pet Insurance
• Employee Stock Purchase Program
• 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