AI-Ops Engineering Lead - Director

Sumitomo Mitsui Banking Corporation•Charlotte, NC

3d•Hybrid

About The Position

As the AI-Ops Engineering Lead in the Platform Engineering team, you will define and drive the strategy for operationalizing, monitoring, and governing the AI/GenAI platform and the models, pipelines, and agents that run on it. You will set the standards, reference patterns, and operating model that the broader engineering organization adopts, and partner with Azure, Databricks, and other infrastructure providers to build and operate the MLOps/LLMOps backbone of the platform. As a senior technical leader, you will influence architecture, technology, data, and business stakeholders, shape the AI-Ops roadmap, and champion operational excellence—reliable, observable, secure, and cost-efficient AI systems—across the enterprise. This role begins as a hands-on technical leader and is expected to grow into building and leading a dedicated AI-Ops team over time. This is a unique opportunity to own and lead the operational excellence of the GenAI technology stack—bridging the gap between one-off experiments and production-grade AI systems—while shaping the practices, governance, and culture that ensure industrial-grade reliability, compliance, and efficiency in a high-stakes financial environment.

Requirements

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.

Responsibilities

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.