Overall Objectives of Job:

We are seeking a hands-on AI Engineer to act as the technical core of the AI Factory squad. A generalist developer who builds in development and, ideally, production environments, the FDE works directly with local and global domain experts to bring AI faster into business processes. The FDE uses existing reusable building blocks (MVS) where they fit and builds custom where they do not, operating effectively without complete specifications. Moving between OEs, stacks, landscapes, and implementation-partner setups, the FDE is spec-driven and prompt-first, ships production-ready code, hands off cleanly, and feeds recurring patterns back into the AI Building Block catalogue so that one OE's solution lifts the next.

Qualifications & Experience

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 4-6 years of overall engineering experience, with strong hands-on platform, infrastructure, or developer-platform building.
• Hands-on software development experience (junior to senior); a generalist across backend, frontend, data, ops, and agent frameworks.
• Demonstrated ability to drop into an unfamiliar codebase, domain, or partner setup and ship working software quickly.
• Comfortable building in both development and production environments, and working without complete specifications.
• Hands-on experience with GenAI / agentic AI development – LLM APIs, prompt engineering, RAG, and agentic frameworks (e.g. LangChain, LangGraph, Semantic Kernel, AutoGen).
• Experience integrating across multiple stacks, cloud landscapes (Azure, AWS, or GCP), and third-party or partner systems.
• Spec-driven, prompt-first working style with clean engineering practices, clean hand-offs, and a habit of turning solutions into reusable building blocks.

Role & Responsibilities

• Act as the technical core of the AI Factory squad, building AI solutions in development and, ideally, production.
• Work directly with local and global domain experts to bring AI faster into business processes.
• Use existing reusable building blocks (MVS) where they fit and build custom where they do not.
• Operate effectively without complete specifications, using a spec-driven, prompt-first approach.
• Move between OEs, stacks, landscapes, and implementation-partner setups as engagements require.
• Build production-ready solutions and hand off cleanly before moving to the next OE engagement.
• Feed recurring patterns back into the MVS / AI Building Block catalogue so each OE's solution lifts the next.
• Reuse what already exists in the building-block catalogue rather than rebuilding from scratch.
• Form a half-pizza team with the AI Engagement Manager for each OE engagement.
• Apply responsible-AI, security, and quality practices in everything that ships.

Key Result Areas

• Production-ready AI solutions shipped across OE engagements.
• Clean, timely hand-offs that move smoothly from build to run.
• New reusable patterns contributed to the MVS / AI Building Block catalogue, and reuse of existing ones.
• Speed to first working solution when dropping into a new codebase, domain, or partner setup.
• Quality, security, and responsible-AI posture of shipped code.