AI Machine Learning Engineer with Strong Python skills

This is a contract to hire role, and the client does not provide any form of visa sponsorship. To qualify, candidates must be eligible to work in US without sponsorship now or in future.

Python is very important. They will have to do an internal tech screening in python prior to being submitted to the hiring manager.

Position Overview:

This 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.

They follow a POC -> MVP -> Prod model. They will be given a use case/business problem, and asked to go build it. They will need to make the design decisions (which data source to use i.e. Snowflake, S3, vector database; which LLM to use, whether to take an AI approach or the problem can be solved with traditional ML) and build a POC.

They need to be able to explain the why behind their designs - what were the trade-offs?

Will need exploratory data analysis skills - they have data in Snowflake (analytics largely), S3 because they can house all datatypes there i.e. call data, structured, unstructured; vector, traditional PostgreSQL. Understand what data they have available.

Will be responsible for LLM/model selection - they primarily use Bedrock models, but have all 3 clouds within their environment so are not limited.

Building RAG, chunking methods, MCP servers, agent orchestration, LangGraph

Traditional ML techniques like classification, anomaly detection, time series.

Should have strong experimentation skills and POC development to prove/disprove use cases, but this team is responsible for taking them through to production so needs production experience as well.

Team size: current team is 8 members: 5 "core" people who can do agentic AI development.

Biggest gap is in the "back-end" side of AI development, RAG, MCP, agents - less on the software side of building the 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)