Lead ML/AI Platform Engineer

Accountabilities

• • Own the ML/AI platform: Lead the architecture and operation of training infrastructure, model serving, inference pipelines, model registries, feature pipelines, and production integrations.
  • Set technical direction: Partner with the Data Platform Architect to define ML/AI architecture, engineering standards, tooling strategies, and build-versus-buy decisions.
  • Build production ML systems: Oversee feature engineering, model training, tuning, registration, deployment, monitoring, and hosted inference using AWS managed services and open-source tooling.
  • Drive GenAI and LLM engineering: Develop practical strategies for RAG, prompt engineering, evaluation, fine-tuning, model serving, and agentic workflows while balancing cost, latency, quality, and safety.
  • Develop agentic capabilities: Evaluate and implement emerging agent technologies and workflow patterns, including MCP, stateless and stateful architectures, and appropriate guardrails for regulated environments.
  • Own the serving layer: Design scalable ML services and API contracts that integrate cleanly with Java microservices, making informed decisions around performance, latency, throughput, and reliability.
  • Enable research-to-production: Partner with Data Science to productionize models and create the infrastructure, pipelines, and deployment processes required to move experimentation into reliable production.
  • Establish MLOps practices: Drive model monitoring, drift detection, reproducibility, experiment tracking, model registries, and cost observability in collaboration with DevOps.
  • Shape the ML roadmap: Work with product, data, and engineering leadership to identify high-impact AI/ML opportunities and translate them into actionable technical roadmaps.
  • Provide technical leadership: Mentor senior engineers, guide complex architectural decisions, and represent the AI/ML function in cross-functional discussions.
  • Communicate technical strategy: Translate complex architectural trade-offs into clear design documents and executive-level recommendations for both technical and non-technical stakeholders.

  Requirements:

  • Senior engineering experience: 8+ years in software or ML engineering, including at least 5 years delivering production machine learning systems and owning complex, ambiguous problems end to end.
  • Technical leadership: Demonstrated experience shaping ML strategy, mentoring senior engineers, and serving as a trusted decision-maker for challenging architectural problems.
  • AWS ML expertise: Hands-on experience with Amazon SageMaker for training, tuning, hosted endpoints, and model registry, as well as Amazon Bedrock and AgentCore for GenAI and agentic applications.
  • Open-source ML tooling: Practical experience with JupyterLab, Spark, MLflow, and related machine learning development and experimentation tools.
  • AWS platform knowledge: Deep familiarity with services including S3, Athena, Redshift, Glue, Step Functions, and Lambda, combined with strong SQL skills for analytical and ML workloads.
  • GenAI/LLM expertise: Production experience with RAG, prompt engineering, evaluation, and the practical trade-offs between quality, cost, latency, and safety.
  • Vector search knowledge: Experience with vector databases such as pgvector or Pinecone, together with good judgment about when vector retrieval is appropriate versus alternative approaches.
  • Programming skills: Deep Python expertise and strong knowledge of core ML libraries such as scikit-learn, pandas, NumPy, PyTorch and/or TensorFlow, and XGBoost or LightGBM.
  • Java familiarity: Sufficient working knowledge of Java to review service code, define API contracts, and troubleshoot integrations with backend microservices.
  • MLOps expertise: Strong understanding of model monitoring, drift detection, reproducibility, experiment tracking, model registries, deployment patterns, and cost observability.
  • Cross-functional collaboration: Comfortable partnering with Data Science, DevOps, backend engineering, product, and leadership while maintaining clear ownership across shared technical boundaries.
  • Communication: Excellent written and verbal communication skills, with the ability to produce both detailed technical designs and concise executive-level recommendations.
  • Contracting requirements: Ability to work independently as an independent contractor through your own entity or an approved contracting arrangement, with reliable overlap with US Pacific business hours.
  • Nice to have: Experience with ClickHouse or similar analytical databases, LLM fine-tuning techniques such as LoRA or QLoRA, streaming and real-time inference, Kafka or Kinesis, infrastructure-as-code, large-scale ML systems, or open-source ML contributions.

  Benefits:

  • Equity compensation package.
  • Flexible Time Off (FTO) to take time away when needed to rest and recharge.
  • Medical, dental, and vision coverage, with 100% of employee premiums covered where applicable.
  • Disability and life insurance.
  • Learning and career development opportunities within a growing technology environment.
  • Remote-work setup reimbursement.
  • Monthly phone and internet stipend.
  • Team-building events, cultural activities, and company-wide gatherings.
  • Paid time off for volunteering and community service.
  • Half-day Fridays.
  • 401(k) matching contribution.
  • Opportunity to work on AI/ML infrastructure supporting products used by more than 1,600 financial institutions.
  • Fully remote work environment as part of a globally distributed team.