Role: AI/MLOps Engineer
Location: Bay Area, CA
Contract
JD:
Key Responsibilities:
• Serve as the primary point of contact for all production AI model issues, ensuring timely troubleshooting and resolution . • Diagnose and fix problems related to model output quality, behavior, hallucination, drift, accuracy, and performance degradation across APIs, pipelines, agents, vector databases, prompt flows, and orchestration layers.
• Conduct comprehensive model testing and validation—including regression testing, performance checks, safety assessments, and scenario-based evaluations—to ensure models meet business expectations before production deployment.
• Collaborate closely with business stakeholders, QA teams, AI engineers, DevOps/MLOps teams, and product teams to ensure smooth communication, effective incident management, and seamless handoffs between development and support functions.
• Implement and maintain monitoring and observability frameworks to track model performance, latency, cost, drift indicators, anomalies, failure events, and user feedback through dashboards and alerting systems.
• As Q/A in AI, collaborate closely with business stakeholders, AI engineers, DevOps/MLOps teams, and product teams to ensure smooth communication, effective incident management, and seamless handoffs between development and support functions.
• Maintain detailed SOPs, RCA logs, runbooks, deployment records, and operational documentation to ensure process consistency and compliance.
• Collaborate closely with business stakeholders, QA teams, AI engineers, DevOps/MLOps teams, and product teams to ensure smooth communication, effective incident management, and seamless handoffs between development and support functions.
Technical Skills
• Strong experience with AI/ML model operations, LLMs, and prompt engineering fundamentals.
• Hands-on experience with Python, APIs, RESTful services, and JSON.
• Knowledge of MLOps pipelines, CI/CD, and model deployment tools.
• Experience with cloud platforms (AWS / Azure / GCP).