Senior Machine Learning Engineer
Core
Lead the transition of experimental models into production-grade services, building the infrastructure for the ML lifecycle from automated training pipelines to real-time inference clusters.
Role type
Senior Machine Learning Engineer (MLOps & Systems)
Builds
Production ML services, automated training pipelines, real-time inference clusters, and secure APIs wrapping ML capabilities.
Domain
Cloud infrastructure, MLOps, AI systems engineering, FinOps
Deliverable
production ML models | infrastructure
Required skills
Production-quality Python, CI/CD practices, REST API design, Cloud environment operations (AWS), Containerization (Docker), ML lifecycle management, Model observability, Cost optimization strategies
Preferred skills
AWS SageMaker, FastAPI, MLOps practices, Event-driven architectures, LLM/GenAI serving, RAG pipelines, Platform-level service design
Technologies
Python, AWS, Docker, FastAPI, SageMaker, CI/CD tools
Responsibilities
Design and own automated Continuous Training and deployment pipelines; Establish telemetry frameworks for model health and drift monitoring; Optimize cloud spend through auto-scaling and spot-instance usage; Integrate models into the product ecosystem via high-performance APIs; Manage versioning strategies for code, data, and model artifacts; Lead adoption of software engineering best practices including testing and code reviews
Seniority
Senior, hands-on IC
