Machine Learning Operations Engineer I

What you'll do

• Develop tools, services, and frameworks to make ML workflows robust, auditable, and usable.
• Identify ML engineer pain points and build effective solutions to improve their development processes.
• Provide stable tooling for engineers to turn research into production-ready prototypes and products.
• Evaluate and integrate open-source and third-party solutions into the existing platform ecosystem.
• Ship automated processes for model fine-tuning, reinforcement learning, and LLM/Agent evaluation.
• Improve observability for LLMs and agents to monitor performance, decay, and drift in production.
• Track emerging tools and frameworks to promote best practices and strengthen team technical expertise.

What we're looking for

• Must have 1+ years of experience in ML infra, ML Ops, ML Engineering, or a similar skillset.
• Must have experience with Kubernetes.
• Must have an understanding of AWS cloud platforms, including EKS, Bedrock, and SageMaker.
• Must be proficient in Python.
• Must have a basic understanding of ML concepts, LLMs, and agents.
• Must be able to debug distributed systems across infrastructure, networking, and application layers.
• Must have excellent communication skills to drive adoption of new tools and best practices.
• Familiarity with distributed computing frameworks and workflow orchestration (e.g., Ray, Airflow) is a big plus (preferred).