Senior AI Solution Engineer _ GSO

We’re seeking a Senior AI Solution engineer to drive AI adoption across one of the service operation business units.

Major responsibility will be to identify, prototype and implement practical AI that delivers measurable business value.

Senior AI solution engineer will be responsible for producing Generative AI solutions, including RAG pipelines and multi-agent systems—to automate workflows and drive operational excellence.

You’ll work closely with solution/data architects, software developers, data engineers, and domain experts to rapidly prototype and deliver scalable, enterprise-grade systems.

This is an individual contributor role requiring strong research skills, deep expertise in AI foundation models, and the ability to translate cutting-edge concepts into impactful solutions for service operation challenges.

A Snapshot of your Day How You’ll Make an Impact (responsibilities of role) Strategic

• Identify and assess AI, GenAI, Agentic AI, and automation opportunities across service operation business units.
• Conduct business workshops and convert operational challenges into AI-enabled use cases focused on automation and productivity enhancement opportunities.
• Rapidly design and develop proof-of-concepts using Azure AI Foundry, Microsoft Copilot, Power Platform, and Mendix.
• Support deployment, monitoring, and continuous improvement of AI solutions.
• Provide technical leadership on AI governance, responsible AI, and adoption of best practices.
• Track emerging technologies and recommend innovations that create business value. Operational
• Design and implement RAG pipelines, agentic workflows, and LLM integrations for tasks such as document understanding, classification, and knowledge assistance.
• Build agent-based applications for planning, tool use, and execution using frameworks like LangGraph, Semantic Kernel, and prompt orchestration tools.
• Architect data pipelines and cloud solutions for training, deployment, and monitoring on Azure/AWS with Docker, Kubernetes, and CI/CD.
• Convert problem statements into prototypes, iterate with stakeholders, and harden into production-ready microservices (FastAPI) with APIs and event-driven workflows.
• Define rigorous evaluation metrics for LLM/ML systems (accuracy, latency, cost, safety), optimize retrieval quality, prompt strategies, and agent policies.