Staff AI-Ops Engineer

Role Description

The Staff AI-Ops Engineer will play a pivotal role in operationalizing, monitoring, and governing the AI/GenAI platform and the models, pipelines, and agents that run on it. They will work closely with stakeholders in architecture, technology, data, and business organizations. They will partner with Azure, Databricks, and other infrastructure providers to build and operate the MLOps/LLMOps backbone of the AI/GenAI platform, ensuring reliable, observable, secure, and cost-efficient AI systems in production.

Role Objectives

• Operationalize the AI Platform
• Build CI/CD and release engineering
• Own governance, versioning and auditability
• Monitoring, drift and cost governance
• Testing, evaluation and validation
• Responsible AI and security controls
• Operational readiness and run management
• Evaluate emerging technology
• Technical mentorship

Qualifications And Skills

• Bachelor’s degree in Computer Science, Machine Learning, Data Science, or related field.
• 5+ years of hands-on experience deploying, operating, and maintaining GenAI or advanced ML models in production environments, with a strong focus on MLOps/LLMOps.
• 3+ years of experience in Python and GenAI frameworks/tools e.g. Databricks Vector Search, Azure AI Search, Azure AI document intelligence, LangGraph, haystack, Llama Index etc.
• Deep, hands-on expertise with MLOps/LLMOps tooling (e.g. MLflow — Prompt Registry, Tracing, Experiments, Model Serving), data platforms (e.g. Databricks, Databricks Asset Bundles) and cloud platforms (e.g. Azure).
• Proven experience developing and deploying RESTful services, containerization, and automated CI/CD systems.
• Demonstrated experience building observability, monitoring, and alerting for AI systems — data and model drift, evaluation metrics, hallucination/grounding health, performance, and cost (FinOps).
• Working knowledge of prompt engineering, embedding models, RAG evaluation, and vector databases sufficient to instrument, test, and monitor GenAI applications.
• Familiarity with AI governance, responsible-AI, and security/privacy controls in a regulated (e.g. financial services) environment.
• Excellent communication and collaboration skills; proven ability to influence and partner with technical and non-technical stakeholders.