Senior Python AI Engineer - Only W2

Role: Senior Python AI Engineer

Location: Remote (US)

Job Type: W2 Contract

Experience Required: 10+ years

JD:

-Knowledge Base Infrastructure

We are hiring a Python Platform Engineer to operate and evolve the infrastructure behind an enterprise knowledge base platform that is moving from a Confluence-focused RAG chatbot into a broader agentic knowledge system . Today, the platform supports Confluence/GitHub ingestion chunking pgvector RAG retrieval FastAPI serving . Over the next phase, we are expanding toward hybrid retrieval (vector + sparse + graph), multi-source ingestion, evaluation pipelines, agent infrastructure, harness and shared chat platform primitives.

What You'll Own:

• 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

• Ingestion infrastructure across current and future content sources

• 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

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

Nice to Have:

• Experience with Qdrant, Neo4j, or other vector/graph infrastructure

• Experience supporting RAG, search, evaluation, or agent platforms

• Experience in enterprise or regulated environments

• Familiarity with Vault, Splunk, Artifactory, ECR

• Comfort using AI-assisted engineering workflows in day-to-day work