Senior Machine Learning Architect

We are seeking a Senior ML Architect to own the technical vision and architectural design of a modern machine learning platform in AWS. This is a senior-level individual contributor role where you will serve as the most senior technical authority on ML platform decisions.

You will work closely with ML Ops, Data Science, Infrastructure, and Data Engineering teams to build a scalable, durable, and production-ready ML ecosystem from the ground up.

This role is ideal for someone who thrives in ambiguous environments, enjoys designing systems from scratch, and can drive technical alignment across cross-functional teams.

Key Responsibilities

Platform Architecture & Technical Vision

• Own end-to-end ML platform architecture including data ingestion, feature engineering, training pipelines, model serving, and observability
• Define and maintain a phased ML architecture roadmap
• Document architectural decisions including trade-offs and rationale
• Ensure scalability as data sources, models, and use cases expand

ML Development Platform Design

• Define ML development standards (environment management, dependency strategy, packaging conventions)
• Architect CI/CD pipelines for the full ML lifecycle
• Establish architectural boundaries between experimentation, staging, and production
• Recommend and standardize ML tooling

Orchestration & Data Flow

• Design ML workflow orchestration strategy (Airflow, Dagster, SageMaker Pipelines, etc.)
• Define data flow architecture from data warehouse through training and inference
• Specify structured intermediate storage and data contracts
• Establish observability and lineage requirements

Deployment & Serving Architecture

• Architect model promotion and deployment strategies (batch & real-time)
• Design feature store and model registry frameworks
• Provide architectural guardrails for AWS infrastructure decisions
• Evaluate AWS-native and third-party ML services

Technical Leadership

• Serve as senior ML architecture authority across teams
• Drive technical alignment without direct management authority
• Partner with Data Science to reduce deployment friction
• Clearly communicate architectural decisions to both technical and non-technical stakeholders

Required Qualifications

• 8 12+ years of experience in ML Engineering, ML Architecture, or ML Platform/Infrastructure
• Proven experience designing ML platforms from scratch in AWS
• Deep AWS expertise (SageMaker, ECS, Lambda, Step Functions, S3, IAM, RDS, etc.)
• Strong Python proficiency
• Experience with ML lifecycle tooling (MLflow, orchestration frameworks, feature stores)
• Experience with Infrastructure-as-Code (Terraform or CloudFormation)
• Strong understanding of production ML systems and model lifecycle management

Preferred Skills

• Experience building scalable ML platforms in enterprise environments
• Strong background bridging data science experimentation and production systems
• Excellent documentation and architectural communication skills