AI Deployment Engineering Manager (all genders)
Kronberg, Campus Kronberg 1
Posted 5d ago
About the job
Accenture Song is seeking a senior technical practitioner to embed directly into client organizations across Central and Eastern Europe. In this role, you will be the technical owner on the ground, responsible for scoping problems, building AI solutions, and demonstrating AI-native delivery. You will work at the intersection of engineering, product thinking, and client delivery, moving from vague client problems to working proofs of concept in days and production-grade deployments in weeks. The expectation is to own outcomes end-to-end, bringing AI tools into real delivery contexts and impact, with a deep understanding of how engineering decisions affect user experience.
Responsibilities
- Design, build, and ship production-grade AI applications, including custom agents, LLM-powered workflows, and sub-agents, integrating with client systems.
- Own the complete delivery stack, including backend logic, API integrations, front-end surfaces, and cloud infrastructure.
- Compress the gap between concept and working software through rapid prototype cycles and driving them into production.
- Connect AI-native capabilities to client's existing platforms by integrating APIs, data pipelines, and enterprise tooling.
- Operate as a trusted technical partner within client environments, attending ceremonies and gaining access to production systems.
- Scope ambiguous problems into tractable engineering challenges and identify where AI-native approaches create leverage.
- Monitor, debug, and optimize deployed systems, handle incidents, and ensure stability before handover.
- Translate complex AI behaviors into transparent, controllable user experiences in collaboration with Experience Designers.
- Communicate technical trade-offs and deployment realities clearly to engineering teams and executive stakeholders.
- Identify and document reusable architectures, integration approaches, and deployment patterns to feed back into the practice.
- Transfer knowledge to client teams through code-with sessions and workshops.
- Surface client needs, production issues, and AI capability readiness to inform offering development.
Requirements
- Extensive experience in engineering production software, with significant client-facing or consulting exposure, or prior forward-deployed engineering experience.
- Demonstrable track record of shipping AI-native systems into production, such as LLM-powered applications, agentic workflows, RAG pipelines, or multi-agent orchestration.
- Full-stack engineering capability with strong backend skills in Python.
- Deep roots in software engineering and an understanding of design principles.
- Ability to go from a vague client problem to a working proof of concept in days, and to production in weeks.