AI Deployment Engineering Manager (all genders)

Description

The company is a global professional services firm with about 791,000 people in more than 120 countries.

Its design and digital products group helps well-known brands rethink their products, services and spaces, from product strategy and human-centred digital experiences to the AI platforms and design systems behind them.

The team works in small, studio-style groups across 70 studios worldwide, with product design, product management, engineering, experience design and content design roles.

AI is the foundation of the work, not an add-on feature.

Responsibilities

• Work inside priority client accounts across Central and Eastern Europe as the hands-on technical owner, scoping problems, building solutions and bringing AI into real use.
• Take a vague client problem to a working proof of concept in days, then to a production-grade deployment in weeks.
• Build and ship production AI applications inside client systems, including custom agents, LLM workflows, MCP servers, sub-agents and agent skills that fit the client's existing technology.
• Own the full stack: backend logic, API integrations, front-end surfaces and cloud infrastructure, and write whatever the work needs.
• Run short prototype cycles, then move the strongest ones to production while keeping the rigour enterprise settings demand.
• Connect AI capabilities to client platforms, APIs, data pipelines, identity systems and enterprise tools so new workflows run within the client's own environment.
• Act as a trusted technical partner: join team ceremonies, gain access to production systems, understand how the organisation works and build the relationships that make deployments succeed.
• Turn unclear problems into workable engineering tasks and judge where AI helps and where it does not.
• Monitor, debug and tune deployed systems, handle incidents, and confirm stability before handing over to the client's own team.
• Turn complex AI behaviour, such as reasoning traces, confidence states, agent actions and fallback flows, into clear, controllable user experiences, working closely with experience designers.
• Explain technical trade-offs and deployment realities to engineers and executives without oversimplifying.
• Document reusable architectures, integration approaches and deployment patterns, and feed them back into the practice.
• Run code-along sessions and workshops so clients can maintain what you built and raise their own technical skills.
• Report what clients ask for, what breaks in production and which AI capabilities are ready for enterprise use, to inform offerings and go-to-market planning.

Requirements

• Extensive experience delivering production software, either as an engineer with substantial client-facing or consulting work or in a prior forward deployed engineering role.
• A record of shipping AI systems to production, such as LLM applications, agentic workflows, RAG pipelines or multi-agent orchestration built with LangChain, LangGraph, LlamaIndex, CrewAI or AutoGen.
• Strong full-stack engineering: backend work in Python as your main language and at least one of TypeScript or Java, plus the ability to build front ends when an engagement needs it.
• Experience deploying to AWS, GCP or Azure through CI/CD pipelines, infrastructure-as-code (Terraform, Helm) and containers (Kubernetes, Docker), and owning production operations end to end.
• Solid data engineering fundamentals: pipelines, structured and unstructured data, and judging data quality in messy real-world settings.
• Experience designing and deploying enterprise AI applications with agentic workflows, RAG pipelines, vector databases, embeddings and semantic search.
• Proven ability to scope ambiguous, real-world problems, move quickly from diagnosis to working software and work independently in unfamiliar client environments.
• Clear cross-functional communication: running design workshops, presenting to technical and executive audiences, writing clear documentation and building trust with client engineering teams.
• Fluent English to work with DACH clients, facilitate workshops and represent the company at the highest level in client environments.
• Bonus: experience building forward deployed engineering artefacts, such as MCP servers, sub-agents, agent skills, custom connectors or production LLM evaluation harnesses.
• Bonus: a prior forward deployed, solutions engineering or technical consulting role at a product company, AI lab or professional services firm, with multi-week client embeddings and end-to-end delivery ownership.
• Bonus: design literacy, including familiarity with design systems, component libraries or human-centred design, and comfort working with product and experience designers as an equal creative partner.
• Bonus: experience with responsible AI in production, including guardrails, content filtering, human-in-the-loop design, auditability and bias evaluation on enterprise systems.
• Bonus: a portfolio of shipped systems rather than slide decks, such as open-source tools, production agents, client case studies or startup work that shows you own outcomes.

Conditions

You will be embedded directly inside client organisations, working alongside their product teams and engineers and taking part in their standups.

You will move from problem framing to prototype to production within a single engagement, then start the next client.

Hiring decisions are made without regard to age, race, religion, sex, national origin, disability, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship or any other legally protected criterion.

Benefits

• Support for career development and flexible working between engagements.
• A culture where your field experience shapes the practice, and a commitment to inclusion and diversity.