Lead ML/AI Platform Engineer (EU, EMEA Remote)

Our company is growing and we are looking for Independent Senior Data Engineer Contractors to help support the growth.

**This Independent Contractor will work 100% Remotely from your home office in Warsaw, Poland as part of a distributed team in the USA, Canada, Europe and several locations in India.

We are growing our team and looking for a Lead ML/AI Platform Engineer.

As a Lead ML/AI Platform Engineer at SavvyMoney, you will own the platform that takes machine learning from experiment to production — training infrastructure, model serving, inference pipelines, and the integration seams with our Java microservices.

You will set technical direction for AI/ML across the company alongside our Data Platform Architect, and drive the engineering side of our GenAI/LLM and agent strategy — from retrieval architectures and evaluation harnesses to the guardrails required to run agentic workflows responsibly in a regulated environment.

• Partner with our Data Platform Architect to set technical direction for AI/ML across the company — architecture, tooling, standards, and build-vs-buy decisions.
• Own the ML/AI platform: training infrastructure, model serving, inference pipelines, and production integration.
• Feature engineering, model training, model registry, and hosted inference in Amazon SageMaker
• GenAI/LLM usage, fine-tuning, and agentic workflows in Amazon Bedrock and AgentCore
• Feedback and data pipelines built on AWS Glue, Lambda, and Step Functions
• Own the serving layer and integrate ML services cleanly with our Java microservices — define the API contracts and make the latency and throughput trade-offs.
• Drive the engineering side of our GenAI/LLM strategy: retrieval architectures, evaluation harnesses, serving patterns, and the judgment calls about which approach fits which problem.
• Bring depth on the emerging agent stack – MCP, agent workflow patterns, stateless and stateful designs, and the guardrails needed to run them responsibly in a regulated environment.
• Partner with our Data Scientist on the handoff from experimentation to production: productionize models, stand up the feature pipelines and serving infrastructure they need, and shorten the loop between training and deployment.
• Work with product, data, and engineering leadership to identify the highest-impact ML opportunities and translate them into roadmaps.
• Represent the AI/ML function in cross-functional forums, communicating trade-offs clearly to technical and non-technical audiences alike.