Machine Learning Engineer

Local candidate only and W2

We’re looking for a Senior Machine Learning Architect to define and own the technical vision of a scalable ML platform built in AWS. This is a senior individual contributor role — you will be the primary technical authority on ML architecture decisions, partnering closely with ML Ops, Data Science, Infrastructure, and Data Engineering teams.

This environment is evolving, with opportunities to build orchestration, deployment architecture, and standardized ML development practices from the ground up.

• Define and maintain a phased ML architecture roadmap
• Document and drive architectural decisions (orchestration, serving patterns, feature store, model registry)
• Ensure scalability as models, data sources, and use cases grow
• Design CI/CD architecture for the full ML lifecycle
• Establish boundaries between experimentation, staging, and production
• Select tooling that enables fast, reliable DS experimentation
• Architect data flow from Snowflake through preprocessing, training, and inference
• Define intermediate storage patterns and data contracts
• Establish lineage, monitoring, and observability standards

• Design AWS-based ML deployment strategy (batch + real-time)
• Define feature store and model registry architecture
• Provide architectural guardrails across ECS, Sage Maker, and related services
• Evaluate AWS-native and third-party tooling
• Serve as senior authority on ML platform decisions
• Drive alignment across engineering, DS, and infrastructure teams
• Translate complex architectural trade-offs for technical and business stakeholders

• 8–12+ years in ML Engineering, ML Architecture, or ML Platform roles
• Proven experience designing ML platforms from scratch in AWS
• Deep AWS expertise (Sage Maker, ECS, Lambda, Step Functions, S3, IAM, RDS)
• Strong Python proficiency and production ML tooling experience (MLflow, orchestration frameworks, feature stores)
• Experience with Terraform or Cloud Formation
• Deep understanding of the full ML lifecycle — from experimentation to production
• Senior-level technical leadership without direct people management
• Ability to set standards across multiple teams
• Strong communication skills and architectural clarity
• Experience scaling ML systems in real-world production environments