GenAI Platform Engineer: Scale, Secure AI Infra & MLOps

SMBC Group; Charlotte (NC); Hybrid; USD 140,000 - 210,000; Full time

Job summary

SMBC Group in Charlotte is seeking a Staff AI/GenAI Engineer to design, develop, and deploy scalable AI platforms within the Platform Engineering team. You will collaborate with architecture, data, and business partners to build secure, compliant GenAI solutions hosted on Databricks and Azure, delivering enterprise-grade capabilities.

Responsibilities include defining architecture, building infra and REST/websocket services, managing MLOps and LLMops, data pipelines, GenAI workflows with RAG,

Qualifications

• Bachelor’s degree in CS, ML, DS, or related field.
• 3+ years deploying GenAI/ML models in production.
• 3+ years Python and GenAI frameworks/tools experience.
• Strong prompt engineering and RAG implementation knowledge.
• Experience with MLOps/LLMOps and ML libraries.

Responsibilities

• Define scalable AI platform architecture using Azure and Databricks.
• Develop platform infra and AI/LLM services for multiple apps.
• Own ML/LLMOps lifecycle, observability, and security.
• Build data pipelines to support AI/ML workloads.
• Develop GenAI workflows including RAG and orchestration layers.
• Evaluate emerging GenAI tech and mentor teams on platform capabilities.

Skills

Python

GenAI frameworks

Databricks

Azure AI

LangGraph

MLOps

REST APIs

CI/CD

Education

Bachelor’s degree in Computer Science or related field

Tools

Databricks Vector Search

Azure AI Search

Azure AI Document Intelligence

LangGraph

haystack

Llama Index

Job description

SMBC Group in Charlotte is seeking a Staff AI/GenAI Engineer to design, develop, and deploy scalable AI platforms within the Platform Engineering team. You will collaborate with architecture, data, and business partners to build secure, compliant GenAI solutions hosted on Databricks and Azure, delivering enterprise-grade capabilities.

Responsibilities include defining architecture, building infra and REST/websocket services, managing MLOps and LLMops, data pipelines, GenAI workflows with RAG,