Remote Lead ML Platform Engineer for GenAI & MLOps

SavvyMoney; United States; Remote; USD 165,000 - 248,000; Full time

Benefits offered by this job

Equity Compensation Package

Flexible Time Off (FTO)

Medical, Dental, Vision – 100% premium

Disability/Life Insurance

Remote work stipend

Monthly stipend for phone and internet

401k matching

Beautiful California East Bay offices

Job summary

SavvyMoney, a US-based fintech serving 1,600+ financial institutions, is seeking a Lead ML/AI Platform Engineer contractor. You will own the platform for training, serving, and pipelines, partnering with the Data Platform Architect to set technical direction for AI/ML across the company.

You will drive GenAI/LLM strategy, integrate with Java microservices, and help productionize models with feature pipelines. This is a 100% remote contract role, overlapping US Pacific hours.

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 (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.

Responsibilities

• 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.

Skills

ML engineering

Distributed ML

AWS SageMaker

Python

Java

MLOps

Data platform architecture

Leadership

Java microservices

SQL

Tools

Amazon SageMaker

Amazon Bedrock

AgentCore

JupyterLab

Spark

MLflow

Python

Java

Job description

SavvyMoney, a US-based fintech serving 1,600+ financial institutions, is seeking a Lead ML/AI Platform Engineer contractor. You will own the platform for training, serving, and pipelines, partnering with the Data Platform Architect to set technical direction for AI/ML across the company.

You will drive GenAI/LLM strategy, integrate with Java microservices, and help productionize models with feature pipelines. This is a 100% remote contract role, overlapping US Pacific hours.