Senior AI Software Engineer

Job Description:
This is a build-from-the-front seat inside one of the biggest names in aerospace and defense. This team is driving the companies move to SAP S/4HANA and standing up the closeout application at the center of that program. They are now fielding a steady stream of requests to build AI solutions, and they need a very senior engineer who can actually deliver them, not just talk about them.

You’d be that person: the one who turns “can we do this with AI?” into working software. LLM-powered applications, MCP servers that connect models to the real enterprise systems running the S/4HANA program, and the integrations and evaluations that make it all reliable enough to trust in a mission-critical environment. Real ownership, direct line to the technical lead, and the kind of problems most engineers only get to read about. If you want to do serious AI engineering where it actually ships and actually matters, this is the one.

Requirements:
• 7+ years professional software development across design, build, integration, testing, debugging, and delivery
• 2+ years hands-on using LLMs to build applications or ship working solutions, covering both AI-assisted development and apps with LLM capabilities built in
• Advanced prompting and context engineering: building and troubleshooting instructions, prompts, reusable skills, task context, and tool descriptions, and understanding how context limits, conflicting instructions, missing info, and tool outputs change results
• Hands-on MCP server development: creating, extending, testing, and troubleshooting servers that connect AI tools to APIs, databases, and enterprise systems
• Production and verification discipline: reviewing and testing generated code, building repeatable AI evaluations to catch regressions, and running deployed apps with an eye on reliability, latency, cost, and secure handling of sensitive data

Nice to have:
• Experience connecting LLM applications to enterprise knowledge through retrieval, embeddings, search, and source grounding.
• Experience delivering applications in enterprise environments with sensitive data and controlled access.

Responsibilities:
• Translate business needs into maintainable applications and AI-enabled solutions.
• Use AI development tools throughout the software lifecycle while retaining responsibility for design decisions, testing, and delivered results.
• Develop prompts, instructions, reusable skills, and context-management approaches suited to the task and model.
• Build and maintain MCP servers and other integrations that give models controlled access to relevant information and tools.
• Design application workflows that coordinate model calls, tool execution, validation, recovery, and human review where needed.
• Compare models and harnesses against representative tasks and use evidence to guide adoption and configuration.
• Establish automated tests and AI evaluations; monitor quality, reliability, latency, and cost after deployment.