> Markdown version of [/jobs/ext/1356970-python-ai-engineer](https://www.wearedevelopers.com/jobs/ext/1356970-python-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Python AI Engineer

- **Company:** WaferWire Cloud Technologies
- **Location:** Salt Lake City, UT, United States
- **Experience:** Expert
- **Salary:** $83,200.0 - $104,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Confluence, Compilers, Encodings, Databases, Extract Transform Load (ETL), Github, Python (Programming Language), Log Analysis, Neo4j, Prometheus, Search Technologies, Vault (Revision Control System), SQLAlchemy, Large Language Models, Grafana, Backend, Fastapi, Kubernetes, Low Latency, Splunk, Data Pipelines, Docker, Artifactory, Golang
- **Published:** July 20, 2026
- **Apply:** https://www.careerjet.com/jobad/usff8597c8cb69d63e47205e821e5433a2

## About the Role

* 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

## Description

* 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


## Related Videos

- [Our journey with Spring Boot in a microservice architecture](https://www.wearedevelopers.com/videos/511-our-journey-with-spring-boot-in-a-microservice-architecture)
- [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [AI Vector Search at Scale - Ewa Szyszka - Ewa Szyszka](https://www.wearedevelopers.com/videos/2161-ai-vector-search-at-scale-ewa-szyszka-ewa-szyszka)
- [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data)
- [Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps)

## Related Articles

- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)