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
