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AI Developer

Not Disclosed

Job Description & Details

AI Developer role based in Denver, on‑site from day one. The team is building generative AI services that power internal tools, so you’ll be deep in model integration, prompt engineering, and production‑grade MLOps. If you’ve spent a decade building Python back‑ends and wrestling with vector stores, this is a straight‑shoot gig.

What You'll Actually Be Doing

You’ll own end‑to‑end pipelines: design FastAPI endpoints, stitch together LLMs (OpenAI, Claude, Llama) with Retrieval‑Augmented Generation, and keep the whole thing running on CI/CD pipelines using MLflow. Expect daily debugging of latency spikes in vector DBs like Pinecone/FAISS, writing SQL queries for reporting, and collaborating with product folks to translate vague AI use‑cases into concrete APIs.

The Core Tech Stack

The stack is Python‑first: heavy use of PyTorch or TensorFlow for model work, Scikit‑learn for classic ML, and FastAPI for the HTTP layer. You’ll need to be comfortable with TypeScript when the front‑end team asks for typed contracts. The real kicker is RAG architecture—so you must know how to embed documents, manage vector stores, and tune retrieval pipelines. MLOps isn’t an afterthought; you’ll be wiring MLflow into GitHub Actions or similar CI tools to automate model versioning and deployment.

Interview Expectations

  1. Design a RAG pipeline that can answer questions over a 10 GB knowledge base with sub‑second latency. The interviewer will look for your approach to chunking, embedding model choice, vector store indexing strategy, and how you’d cache results in production. 2. Explain how you would containerize a PyTorch model and set up a rolling update without dropping in‑flight requests. They want to see your grasp of Docker, orchestrators (K8s or ECS), and zero‑downtime deployment patterns, plus how you’d monitor drift with MLflow.

Application Advice

Tailor your resume to hit every must‑have keyword: Python, AI/ML, Generative AI, TypeScript, FastAPI, OpenAI/Claude/Llama, PyTorch/TensorFlow, Scikit‑learn, RAG, Pinecone/FAISS, MLOps, MLflow, CI/CD, REST APIs, and SQL. Highlight any on‑site or hybrid experience in Denver to reassure they can slot you in immediately. If you have visa sponsorship experience, surface it early— the posting explicitly asks for visa type.