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

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.

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.

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.

Data Scientist — leads feature engineering, model training, fine-tuning, and experimentation. You'll partner on the research-to-production handoff, stand up the infrastructure they need, and jointly own the quality of what ships.

DevOps — owns CI/CD, deployment infrastructure, and platform observability. You'll partner on ML-specific extensions (model artifacts, reproducible environments, canary and shadow deployments) rather than rebuilding what already exists.

Backend engineers — own the Java microservices that consume ML outputs. You'll define the contracts and own the serving side.

Product and engineering leadership — you're the technical voice on what we should build with AI/ML, and how.

Distributed processing: AWS Glue

Orchestration: AWS Lambda, AWS Step Functions

Managed ML: Amazon SageMaker, Amazon Bedrock, AgentCore

Open-source ML: JupyterLab, Spark, MLflow

Storage & query: Amazon S3 (data lake), Amazon Athena, Amazon Redshift, ClickHouse

Languages: Python (ML, pipelines), Java (microservices) Why This Role Matters Credit data is the core of what we do, and the opportunity to make it smarter — better offer targeting, better personalization, better insight for 1,600+ financial institutions and the consumers they serve — runs directly through the ML platform. Today that platform is early. You will be the person who defines what it becomes. Your decisions on architecture, tooling, and evaluation will set the ceiling on what every ML-driven feature at SavvyMoney can do for years. Join us at SavvyMoney and help build the AI foundation of a platform trusted by 1,600+ banks and credit unions.

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

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