Role Objectives

• Set AI-Ops strategy and standards: Define the enterprise vision, operating model, and reference patterns for MLOps/LLMOps on Databricks and Azure Cloud Services, and drive their adoption across engineering, architecture, and data teams.
• Operationalize the AI Platform: Own the MLOps/LLMOps backbone for the AI platform, standardizing how models, prompts, pipelines, and agents are built, promoted, and run reliably in production.
• Build CI/CD and release engineering: Establish automated CI/CD pipelines and infrastructure-as-code for models, prompts, and agents using Databricks Asset Bundles across DEV/QA/REL/PROD, with canary, blue/green, shadow, and automated-rollback deployment strategies.
• Own governance, versioning and auditability: Implement end-to-end lineage and version control across data, prompts, retrievals, models, and responses using MLflow (Prompt Registry, Tracing, Experiments/Runs), delivering audit-ready artifacts and enforceable quality gates for internal and regulatory review.
• Monitoring, drift and cost governance: Build observability for data quality, data and model drift, retrieval and hallucination/grounding health, application performance, and business KPIs, with cost visibility, inference optimization, and FinOps-aligned governance.
• Testing, evaluation and validation: Establish automated regression, A/B, canary, shadow, and champion-challenger validation with golden datasets, evaluation rubrics, and human-in-the-loop review to certify quality and safety before and after release.
• Drive responsible AI, security and governance adoption: Partner with architecture, risk, security, and business leaders to embed responsible-AI guardrails (bias/harm detection, explainability, safety) and security/privacy controls into the enterprise path-to-production.
• Operational readiness and run management: Ensure reliable day-2 operations through model cards, API/SLA contracts, runbooks, incident response, and escalation readiness.
• Evaluate emerging technology: Proactively identify and evaluate emerging AI-Ops tooling and integrate those that improve reliability, observability, and cost efficiency.
• Technical leadership and team building: Mentor and uplift broader engineering teams on MLOps/LLMOps best practices, establish the AI-Ops discipline, and build and eventually lead a dedicated AI-Ops team as the function scales.

Qualifications and Skills

• Bachelor’s degree in Computer Science, Machine Learning, Data Science, or related field (advanced degree a plus).
• 8+ years of hands-on experience deploying, operating, and maintaining GenAI or advanced ML models in production environments, including 3+ years in a technical leadership, lead engineer, or architect capacity.
  Demonstrated ability to set technical direction, define standards, and drive cross-functional adoption of MLOps/LLMOps practices at enterprise scale.
• 3+ years of experience in Python and GenAI frameworks/tools e.g. Databricks Vector Search, Azure AI Search, 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).
• Demonstrated experience developing and deploying RESTful services, containerization, and automated CI/CD systems.
• Proven 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.
• Working knowledge of ML libraries e.g. PyTorch, TensorFlow, Hugging Face Transformers.
• Experience mentoring engineers, with the ability and appetite to build and lead a dedicated AI-Ops team as the function grows.
• Familiarity with AI governance, responsible-AI, and security/privacy controls in a regulated (e.g. financial services) environment.
• Executive-level communication and collaboration skills; proven ability to influence and partner with senior technical and non-technical stakeholders.