Machine Learning Operations Engineer II

What you'll do

• Develop tools, services, and frameworks to make every stage of the ML workflow robust and auditable.
• Collaborate with ML engineers to identify pain points and build effective solutions for their specific workflows.
• Provide stable tooling and training to help teams transition research into production-ready products.
• Evaluate and integrate open-source and third-party solutions into the existing platform ecosystem.
• Build automated processes for model fine-tuning, reinforcement learning, and LLM/Agent evaluation.
• Improve observability for agentic applications to monitor performance, decay, and drift in production.
• Track emerging tools and frameworks to maintain technical expertise at the frontier of ML technology.

What we're looking for

• At least 2 years of experience in ML infrastructure, MLOps, or ML engineering.
• Experience managing distributed systems with Kubernetes and understanding its trade-offs.
• Proficiency in Python for development.
• Understanding of AWS cloud platform services including EKS, Bedrock, and SageMaker.
• Familiarity with distributed computing frameworks and workflow orchestration tools like Ray and Airflow.
• Knowledge of software engineering best practices within an ML context.
• Basic understanding of machine learning concepts, LLMs, and agentic systems.
• Ability to debug distributed systems across infrastructure, networking, and application layers.