Efficiency AI Ops Engineer Hybrid
Required skills
Bachelor’s degree with 6+ years, Master’s degree with 4+ years, or PhD with 1+ year of related experience in Computer Science, AI/ML, or a related technical field Proven experience conducting applied research to solve complex technical problems, including the evaluation and integration of new AI/ML technologies Solid conceptual and practical knowledge of AI/ML technologies, including LLMs, model fine-tuning, and their application in production environments Demonstrated experience in software development with a focus on building, testing, and deploying scalable features in a production environment Hands-on experience using AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code) to build, validate, and deploy production-level software artifacts Strong critical thinking skills with the ability to evaluate and document technical trade-offs regarding cost, quality, and performance
Preferred skills
Experience in AI Ops, MLOps, or building/fine-tuning custom LLMs to achieve well defined metrics Proven track record of leading technical projects and mentoring junior engineers Strong communication and collaboration skills; ability to influence multi-functional partners and work effectively within a team environment Experience with "Design Thinking" principles, specifically applying system-level design to AI-driven user experiences Hands-on experience with real-time AI workload management and token optimization strategies Experience building and productizing agentic applications with focus on evaluation and business acceptance
Technologies
LLMs, model fine-tuning, AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code), AI Ops, MLOps, agentic applications, real-time AI workload management, token optimization strategies
Responsibilities
Conduct applied research to evaluate and implement new AI techniques, such as automatic model routing and batch capacity management, to optimize cost and performance Propose and evaluate results of fine-tuning open source LLMs with sharply defined goal metrics. Compare these with similar out of box industry models Build, integrate, and maintain AI agents and features within the Circuit platform to drive process automation across the company Apply Agentic AI to modernize existing features in Circuit platform Continuously monitor the AI landscape to stack-rank and integrate new models from vendors while de-prioritizing underperforming technologies Serve as a "Team Captain" for specific features, driving consensus across multi-functional teams and mentoring peers to elevate the team’s collective technical proficiency Lead the delivery of high-quality design features from initial concept through to production integration and performance measurement
Seniority
Not specified
Domain
AI/ML, AI Ops, MLOps, LLMs, Agentic AI, AI-driven user experiences, AI workload management, token optimization, AI platform development