Machine Learning Ops Engineer

Job description

Job responsibilities

• Design and build new LLM-powered tools and agentic workflows that automate real work and improve productivity across the company
• Extend and improve our RAG systems—ingestion, chunking, embedding, retrieval, ranking, and evaluation—to raise answer quality
• Structure retrieval around the organization's information hierarchy so that relevance and access boundaries improve together
• Build tool integrations that connect LLMs to internal systems and data sources
• Design agents that act safely against real systems, with appropriate guardrails, human-in-the-loop where warranted, and clear failure behavior
• Establish evaluation and testing frameworks to measure quality, catch regressions, and guide iteration
• Partner with teams across the company to identify high-value use cases and turn them into deployed tools
• Deploy AI tools and services for teams across the company, taking them from prototype to reliable production

Job requirements

• Bachelor's in Computer Science, Software Engineering, Data Engineering, or related field – equivalent industry experience also welcome
• 3-6+ years of experience in MLOps, software, platform, or backend engineering (relevant depth matters more than exact years)
• Strong proficiency in Python and comfort building, shipping, and operating services
• Experience building LLM-powered applications—working with LLM APIs or self-hosted models, prompts, and tool/function calling
• Hands-on experience with Kubernetes and containerized deployment
• Solid understanding of CI/CD, infrastructure-as-code, and production service reliability
• Awareness of access control and data-boundary concerns when connecting tools to sensitive internal systems
• Demonstrated ability to learn quickly and work across unfamiliar parts of the stack