Lead ML/AI Platform Engineer (EU, EMEA Remote)
at SavvyMoney · 101-250 employees
Equity · $225M · Oct 2025
- Seniority
- Lead Manager
- Employment
- Full Time
- Location
- Europe
- Posted
- 5d ago
pythonjavarestkafkaawsterraformlambdasparktensorflowpytorchllmci/cd
at SavvyMoney · 101-250 employees
SavvyMoney provides integrated credit-score, financial wellness, and personalized lending tools to more than 1,500 banks, credit unions, and fintechs.
SavvyMoney is a US based leading financial technology company. We provide integrated credit score and personal finance solutions to 1,600 + bank and credit union partners throughout the United States. The SavvyMoney solutions integrate with more than 43 digital banking platforms. SavvyMoney was recently recognized by the San Francisco Business Times and the Silicon Valley Journal as one of the "Top 25 Places to Work in the San Francisco Bay Area" and is an Inc. 5000 Fastest Growing Company. 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. JOB DESCRIPTION OVERVIEW 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. WHAT YOU'LL DO - 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. WHAT WE'RE LOOKING FOR Required - 8+ years in software or ML engineering, including 5+ years shipping production ML systems and a track record of owning ambiguous, high-scope problems end to end. - Demonstrated technical leadership: you've shaped the ML strategy of a team or organization, mentored senior engineers, and been the person others rely on for difficult architectural calls. - Hands-on experience with both operating models we use: - AWS managed ML stack: Amazon SageMaker (training, tuning, hosted endpoints, model registry), Amazon Bedrock, and AgentCore for GenAI and agentic workflows. - Open-source ML tooling: JupyterLab for notebooks, Spark for distributed processing, MLflow for experiment tracking and model registry. - Deep working knowledge of the AWS stack — S3, Athena, Redshift, Glue, Step Functions, Lambda — plus SQL skills strong enough to model data for both analytical and ML workloads. - Production experience with GenAI/LLMs: RAG, prompt engineering, evaluation, and a clear grasp of the cost, latency, and safety trade-offs involved. - Familiarity with vector databases (e.g., pgvector, Pinecone) and sound judgment on when they're warranted versus alternatives such as NoSQL retrieval. - Working knowledge of Java sufficient to review service code, define API contracts, and debug integration issues with our microservices. - Deep expertise in Python and the core ML stack: scikit-learn, pandas, NumPy, PyTorch and/or TensorFlow, XGBoost / LightGBM. - Solid MLOps fundamentals — model monitoring, drift detection, reproducibility, experiment tracking, model registry, and cost observability — plus the ability to partner with DevOps on CI/CD rather than build it from scratch. - Excellent written and verbal communication — you can write both the design doc that aligns a dozen engineers and the one-pager that aligns the exec team. - Strong collaborator, comfortable operating in a role where scope is shared: you'll partner with a Data Scientist on models and DevOps on infrastructure, and you can navigate those seams while keeping clear ownership. - Ability to operate as an independent contractor through your own entity or an approved contracting arrangement, with reliable overlap with US Pacific business hours for architecture reviews and cross-team work. Nice to Have - Experience with ClickHouse or a comparable columnar / real-time analytical database. - Fine-tuning experience (LoRA / QLoRA, instruction tuning, or RLHF). - Streaming and real-time inference experience (Kafka, Kinesis, low-latency serving). - Infrastructure-as-code (Terraform, AWS CDK, CloudFormation). - Experience operating ML systems at meaningful scale — hundreds of millions of predictions per day, or equivalent. - Open-source contributions, conference talks, papers, or patents in ML / applied ML. WHO