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We are seeking a skilled Python Platform Engineer to operate and evolve the core infrastructure powering our enterprise knowledge base platform. As we transition from a Confluence-focused RAG chatbot into a highly advanced, multi-source agentic knowledge system, you will play a pivotal role in designing and scaling our AI backend.
Today, our platform handles automated ingestion (from sources like internal wikis and code repositories) → chunking → pgvector storage → RAG retrieval → FastAPI serving. In this next phase, you will help us expand towards hybrid retrieval (combining vector, sparse, and graph search), multi-source ingestion pipelines, robust evaluation frameworks, and scalable agent infrastructure.
Req.#1050282307
- Architect and implement highly performant backend services using Python 3.11, FastAPI, Pydantic, SQLAlchemy async, and asyncpg
- Design critical retrieval trade-offs optimizing for quality, latency, operational cost, safety, and simplicity
- Build production-grade agent runtime capabilities including memory boundaries, tool sandboxing, granular permissions, and cost/budget controls
- Improve answer grounding, failure analysis, and citation enforcement (prioritizing robust production behavior over simple demo-only features)
- Create production-grade observability and feedback loops utilizing OpenTelemetry, Prometheus, Grafana, Docker, Helm, and GitHub Actions
- Partner closely with product and engineering teams to support multiple conversational surfaces through a unified knowledge platform
- Overhaul ingestion pipelines, manage AI workload profiles (handling latency, throughput, and failovers), and implement release workflows that validate complex AI behavior
- Strong, hands-on experience developing in Python within platform, automation, or infrastructure-heavy environments
- Proven experience building CLI tools utilizing Python, Golang, or Rust
- Deep experience working with LangGraph, LangChain, pgvector, and modern RAG/retrieval pipelines
- Experience designing and implementation-level knowledge of evaluation frameworks for LLM-backed systems (including regression detection and quality benchmarking)
- Strong experience with Docker, Helm, GitHub Actions, and Kubernetes-oriented container orchestrations
- Solid understanding of the operational characteristics, scaling bottlenecks, and cost profiles of embedding pipelines, vector search, and LLM providers
- Strong observability skills spanning metrics, tracing, alerting, dashboarding, and log analysis
- Experience managing ingestion, ETL, or large-scale content processing pipelines
- Experience with specialized vector or graph infrastructure (e.g., Qdrant, Neo4j)
- Past experience supporting search platforms, RAG systems, or agent-based platforms in enterprise or highly regulated environments
- Familiarity with enterprise-grade tooling (e.g., Vault, Splunk, Artifactory, ECR)
- Comfort leveraging modern AI-assisted engineering tools (Copilot, etc.) to enhance your day-to-day coding workflow