AI Machine Learning Engineer with Strong Python skills

Position Overview

• This is a contract‑to‑hire role. The client does not provide any form of visa sponsorship. Candidates must be eligible to work in the US without sponsorship now or in the future.
• Python is very important. Candidates will have to complete an internal tech screening in Python prior to being submitted to the hiring manager.
• The team is responsible for end‑to‑end development of ML and AI products. They have a number of different use cases spanning agentic AI, RAG, classification, etc., and do not have the right‑sized team to deliver on all of them.
• The ideal candidate will have a 70‑30 breakdown of AI Engineering to traditional ML.
• Use cases include: natural language chatbot for querying data, classification model for ticketing system.
• The team follows a POC → MVP → Prod model. Candidates will be given a use case/business problem and asked to build it, making design decisions (e.g., data source: Snowflake, S3, vector database; LLM selection; AI vs. traditional ML approach) and building a POC.
• Candidates need to be able to explain the why behind their designs—what were the trade‑offs?
• Must have exploratory data analysis skills; data resides in Snowflake (analytics), S3 (all datatypes: call data, structured, unstructured), vector stores, and traditional PostgreSQL.
• Responsible for LLM/model selection—primarily Bedrock models, but all three clouds are available.
• Work includes building RAG, chunking methods, MCP servers, agent orchestration, LangGraph.
• Traditional ML techniques: classification, anomaly detection, time series.
• Strong experimentation skills and POC development to prove/disprove use cases; also production experience is required as the team takes projects through to production.
• Team size: 8 members (5 “core” people who can do agentic AI development).
• Biggest gap: “back‑end” side of AI development (RAG, MCP, agents). Less focus on software side of building Streamlit front‑ends and APIs (aside from calling the agents). Nice to have additional skills there, but the team can pick up.

Must Haves

• Python
• SQL
• Generative AI development: RAG, MCP, LLMs, agentic development and orchestration (LangGraph or similar)
• Understanding of different data sources: warehouse, vector database
• Machine Learning: classification, time series, anomaly detection, etc. (won’t need all of these, but along these lines)
• Strong hands‑on delivery

Nice to Haves

• Software Engineering – UI development (Streamlit)