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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
- 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
- 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
- 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