As a Staff AI-Ops Engineer in the Platform Engineering team, you will play a pivotal role in operationalizing, monitoring, and governing the AI/GenAI platform and the models, pipelines, and agents that run on it.
Responsibilities
Design and operate the MLOps/LLMOps backbone for the AI platform on Databricks and Azure Cloud Services
Develop automated CI/CD pipelines and infrastructure-as-code for models, prompts, and agents using Databricks Asset Bundles
Implement end-to-end lineage and version control across data, prompts, retrievals, models, and responses using MLflow
Build observability for data quality, data and model drift, retrieval and hallucination/grounding health, application performance, and business KPIs
Establish automated regression, A/B, canary, shadow, and champion-challenger validation
Requirements
Bachelor’s degree in Computer Science, Machine Learning, Data Science, or related field
Design and develop advanced AI solutions leveraging large language models and generative AI for industrial automation.
Responsibilities
Design and development of advanced AI solutions – building applications based on large language models (LLMs) and generative AI, contributing to solution architecture, including the use of RAG patterns and LLMOps practices.
Development of robust backend solutions in Python – using modern technologies and frameworks for API development, data transformation pipelines, and interactive tools; implementing testing strategies and ensuring application security, reliability, and performance.
Azure environments and operations – working hands‑on with the team to improve how our Azure environments are structured, configured, monitored, and maintained, while introducing consistent practices for their day‑to‑day management.
DevOps and cloud integration – containerization, deployment to Azure environments, reverse proxy configuration (NGINX), CI/CD pipeline setup, version control management, API gateway integration, including MCP‑based integrations, and deployment and lifecycle management of AI models and solutions using Azure AI Foundry.
Data and API design – working with relational and NoSQL databases, designing schemas, optimizing queries, and integrating REST‑based services with a strong focus on security, performance, and scalability.
Architect, build, and launch robust enterprise-level artificial intelligence solutions leveraging the Microsoft Azure AI Foundry ecosystem, focusing heavily on Intelligent Document Processing (IDP).
Responsibilities
Architect and engineer enterprise AI and Generative AI applications utilizing Azure AI Foundry and Azure OpenAI.
Create Agentic AI systems, AI copilots, conversational chatbots, and RAG-driven solutions using frameworks such as LangChain, Semantic Kernel, and Copilot Studio.
Engineer advanced OCR and IDP workflows capable of managing structured, semi-structured, and unstructured data formats.
Execute document classification, entity recognition, key-value pair extraction, handwriting analysis, and the extraction of data from multi-page tables.
Leverage Azure AI Document Intelligence, Computer Vision, Form Recognizer, and proprietary OCR frameworks to drive solutions.
Requirements
Bachelor's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or a closely related field
Master's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or a closely related field
Design and develop advanced AI solutions leveraging large language models and generative AI, build backend services in Python, manage Azure environments, and lead technical collaboration within the team.
Responsibilities
Design and development of advanced AI solutions – building applications based on large language models (LLMs) and generative AI, contributing to solution architecture, including the use of RAG patterns and LLMOps practices.
Development of robust backend solutions in Python – using modern technologies and frame-works for API development, data transformation pipelines, and interactive tools; implementing testing strategies and ensuring application security, reliability, and performance.
Azure environments and operations – working hands-on with the team to improve how our Azure environments are structured, configured, monitored, and maintained, while introducing consistent practices for their day-to-day management.
DevOps and cloud integration – containerization, deployment to Azure environments, reverse proxy configuration (NGINX), CI/CD pipeline setup, version control management, API gateway integration, including MCP-based integrations, and deployment and lifecycle management of AI models and solutions using Azure AI Foundry.
Data and API design – working with relational and NoSQL databases, designing schemas, optimizing queries, and integrating REST-based services with a strong focus on security, performance, and scalability.
Requirements
University or high school education in IT, software engineering, or a related technical field
Staff AI Platform EngineerNewSMBC · Charlotte, US
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