Staff GenAI Platform Engineer - AI-Ops & MLOps

SMBC Group; Charlotte (NC); Hybrid; USD 180,000 - 260,000; Full time

Job summary

SMBC Group is seeking a Staff AI-Ops Engineer to operationalize and govern its GenAI platform, ensuring reliable, scalable AI systems for critical financial services. You will partner with Azure and Databricks to automate pipelines, enforce governance, and build observability across data and models.

You will design end-to-end MLOps tooling, implement CI/CD for models and prompts, and lead security, privacy, and responsible-AI controls while mentoring teams and driving platform excellence in

Qualifications

• 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).
• 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.
• 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.

Responsibilities

• Operationalize the AI Platform: Design and operate the MLOps/LLMOps backbone for the AI platform on Databricks and Azure Cloud Services, standardizing how models, prompts, pipelines, and agents are built, promoted, and run.
• Build CI/CD and release engineering: Develop 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.
• Responsible AI and security controls: Operationalize responsible-AI guardrails (bias/harm detection, explainability, safety) and security/privacy controls—authentication, authorization, secret management, and protection of data, models, prompts, and embeddings—across the inference and agent-tool layers.
• Operational readiness and run management: Support 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 mentorship: Mentor and educate broader engineering teams on MLOps/LLMOps best practices and platform operational capabilities.

Skills

Python

GenAI frameworks

Databricks

Azure Cloud

MLflow

CI/CD

Observability

Education

Bachelor’s degree in Computer Science, ML, Data Science

Tools

Databricks

Azure

PyTorch

TensorFlow

Hugging Face

Job description

SMBC Group is seeking a Staff AI-Ops Engineer to operationalize and govern its GenAI platform, ensuring reliable, scalable AI systems for critical financial services. You will partner with Azure and Databricks to automate pipelines, enforce governance, and build observability across data and models.

You will design end-to-end MLOps tooling, implement CI/CD for models and prompts, and lead security, privacy, and responsible-AI controls while mentoring teams and driving platform excellence in