Senior MLOps Engineer
Core
Provide technical leadership and hands-on guidance to data scientists and ML engineers on architecture, tooling, debugging, and production design for machine learning systems.
Role type
Senior MLOps Engineer
Builds
Reproducible, productionized machine learning systems and closed-loop experimentation pipelines
Domain
Cloud infrastructure (AWS) and Machine Learning Operations
Deliverable
production ML models
Required skills
MLOps, DevOps, software engineering, AWS SageMaker, EC2, EKS, Lambda, CI/CD, model registries, orchestration platforms, event-driven systems, distributed systems, cloud cost optimization, infrastructure security, IAM/access management
Preferred skills
Technical leadership, clean code, continuous improvement, collaboration
Technologies
AWS, SageMaker, EC2, EKS, Lambda, Metaflow, MLflow, Terraform, Kubernetes
Responsibilities
Architect the transition from data science experimentation to reproducible, productionized machine learning systems; Establish shared patterns for model deployment, versioning, release management, and reusable ML assets; Build closed-loop experimentation using live model telemetry; Optimize ML infrastructure and model-serving costs through rightsizing, caching, and spend visibility; Identify and remediate dependency, access, credential, and secure-deployment risks; Support engineer growth through pairing, technical guidance, and constructive code and design review
Seniority
Senior, hands-on IC
