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# Python AI Engineer

- **Company:** EPAM Systems, Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Salary:** $130,000.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Compilers, Encodings, Databases, Extract Transform Load (ETL), Disaster Recovery, Github, Python (Programming Language), Log Analysis, Neo4j, Regression Testing, Prometheus, Search Technologies, Vault (Revision Control System), SQLAlchemy, Qdrant, LangChain, Retrieval-Augmented Generation, Large Language Models, Grafana, Backend, Fastapi, Pgvector, Kubernetes, Low Latency, Machine Learning Operations, Splunk, Data Pipelines, Docker, Artifactory, Golang
- **Published:** November 7, 2026
- **Apply:** https://dejobs.org/x/x/AF9CC250A167462A89F79519648F4729/job/

## About the Role

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., * 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

## Description

* 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