Python Platform Engineer

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

Responsibilities

• 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

Requirements

• 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

Nice to have

• 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

Benefits

• Extended Healthcare with Prescription Drugs, Dental and Vision, and Healthcare Spending Account (Company Paid)

• Life and AD&D Insurance (Company Paid)

• Employee Assistance Program (Company Paid)

• Telehealth (Company Paid)

• Short-term Disability (Company Paid)

• Long-Term Disability

• Paid Time Off (including vacation and sick days)

• Registered Retirement Savings Plan (RRSP) with Company match

• Maternity/Parental/Adoption Leave Top-up

• Employee Stock Purchase Program

• Critical Illness Insurance

• Employee Discounts

• Unlimited access to LinkedIn learning solutions