Lead, AI Engineering

Location: Berlin or Munich, Germany, or Zurich, Switzerland. At least three days per week must be worked in person at a Bain office or client location; travel beyond the primary workplace is required.

Company

Bain & Company’s AI, Insights & Solutions team designs and delivers AI-powered solutions that create measurable business impact for clients.

What you will do

• Design, build, and deploy end-to-end generative AI systems, multi-agent workflows, and production-grade AI applications.
• Develop multi-component AI pipelines using RAG, fine-tuning, embeddings, hybrid retrieval, reasoning, tool use, function calling, and orchestration.
• Build APIs, microservices, CI/CD pipelines, cloud-native deployments, and GenAIOps processes with strong observability and reliability.
• Design evaluation frameworks and automated evaluation suites covering factual consistency, relevance, latency, throughput, cost, and continual model improvement.
• Implement guardrails, fallbacks, red-teaming, human-in-the-loop workflows, and responsible AI controls in partnership with ethics and infrastructure teams.
• Collaborate with product, engineering, data science, ethics, infrastructure, and client stakeholders; communicate technical findings and share knowledge with peers.

Requirements

• 3–5+ years of hands-on experience in software engineering, ML engineering, or applied AI roles.
• German proficiency at C1 level or higher.
• Experience shipping generative AI features or systems end-to-end, from prototyping through production.
• Strong prompt engineering, context engineering, conversation design, system design, architecture, and production deployment skills.
• Working knowledge of evaluation design, experimentation frameworks, data labelling strategies, and cost and latency trade-offs for LLM applications.
• Experience with RAG architectures, vector-based retrieval, agentic patterns, tool use, routing, memory management, ReAct, RLAIF, HITL, and feedback loops.
• Experience with orchestration frameworks, vector and graph databases, and model and API ecosystems.
• Clear communication and effective collaboration across engineering, product, and data science teams.
• Ability to work in high-ambiguity environments and eagerness to grow into technical leadership while supporting junior team members.

Nice to have

• Experience with hybrid or graph-based retrieval and multi-agent systems.
• Experience supporting or informally mentoring peers.
• Experience in client-facing consulting or enterprise transformation environments.

Culture & Benefits

• Work in multidisciplinary teams using Bain’s apprenticeship model, with support from experienced teammates, frequent feedback, and increasing responsibility.
• Access significant learning and growth opportunities through varied enterprise AI problems and client impact.
• Work collaboratively with major AI ecosystem partners on real client deployments.
• Work in person at least three days per week at a Bain home office or client location, with project-based travel required.