Senior Python AI Engineer - Only W2

About the role

Senior Python AI Engineer – Remote (US) – W2 Contract – 10+ years experience. You will operate and evolve the infrastructure behind an enterprise knowledge base platform that is transitioning from a Confluence‑focused RAG chatbot to a broader agentic knowledge system. The current stack includes Confluence/GitHub ingestion, chunking, pgvector RAG retrieval, and FastAPI serving. Future work will expand to hybrid retrieval (vector + sparse + graph), multi‑source ingestion, evaluation pipelines, agent infrastructure, harness, and shared chat platform primitives.

Responsibilities

• Architect and implement backend services in Python 3.11, FastAPI, Pydantic, SQLAlchemy async, and asyncpg
• Design retrieval and orchestration trade‑offs around quality, latency, cost, safety, and operational simplicity
• Build production‑grade agent runtime capabilities: memory boundaries, tool sandboxing, permissions, and budget controls
• Improve answer grounding, failure analysis, and citation enforcement rather than optimizing for demo behavior
• Create observability and operational feedback loops with OpenTelemetry, Prometheus/Grafana, Docker/Helm, and GitHub Actions
• Work closely with product and engineering partners to support multiple conversational surfaces through one knowledge platform
• Develop ingestion infrastructure across current and future content sources
• Ensure observability across application, pipeline, database, and model‑serving behavior
• Manage cost, latency, throughput, and failure‑mode for AI‑heavy workloads
• Establish release workflows that validate AI behavior changes, not just code compilation

Requirements

• 10+ years of professional experience
• Remote (US) position
• W2 Contract arrangement

Qualifications

• Strong hands‑on experience with Python 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
• Familiarity with the operational characteristics of embedding pipelines, vector search, and LLM‑backed systems
• Strong observability skills across metrics, tracing, dashboards, alerting, and log analysis
• Experience with ingestion, ETL, or content‑processing pipelines at scale
• Ability to think in terms of reliability, cost, latency, throughput, and recovery

Skills

• Python 3.11, FastAPI, Pydantic, SQLAlchemy async, asyncpg
• LangGraph, LangChain, pgvector
• Evaluation framework design for LLM systems
• Docker, Helm, GitHub Actions, Kubernetes
• Embedding pipelines, vector search, LLM‑backed systems
• Observability: OpenTelemetry, Prometheus, Grafana
• Ingestion/ETL pipelines at scale
• Reliability, cost, latency, throughput, recovery engineering
• Nice‑to‑have: Qdrant, Neo4j, RAG, search, evaluation, agent platforms
• Nice‑to‑have: Enterprise or regulated environments, Vault, Splunk, Artifactory, ECR
• Nice‑to‑have: AI‑assisted engineering workflows