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.