[Hiring] Lead ML/AI Platform Engineer @SavvyMoney

Role Description

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.

Qualifications

• 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, Amazon Bedrock, and AgentCore for GenAI and agentic workflows.
• Open-source ML tooling: JupyterLab, Spark, MLflow.
• 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.

Requirements

• 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.

Benefits

• Equity Compensation Package
• Flexible Time Off (FTO) - take time off as needed to rest and recharge.
• Medical, Dental, Vision – 100% premium paid for employee
• Disability/Life Insurance
• Opportunity for learning and career growth with a top Bay Area technology company
• Reimbursement for remote work setup
• Monthly stipend for phone and internet
• Team building events, culture activities, all hands events
• Paid time off to volunteer and serve the community
• Half day Fridays
• 401k matching contribution
• Beautiful California East Bay offices in Dublin, CA