Lead AI Engineer – Generative AI & Agentic Systems

About the role

Bain's AI, Insights & Solutions (AIS) team designs and delivers AI-powered solutions for clients, working in multidisciplinary teams with consultants, product, design, architecture and engineering experts. As Lead AI Engineer, you will design, build and ship generative AI systems and agentic solutions, contributing across the full lifecycle from prototyping to production deployment. This is a hands-on individual contributor role with growing influence on technical direction and the chance to mentor junior team members.

Key responsibilities

• Design, develop and deploy end-to-end generative AI systems, including multi-agent workflows and production-grade applications
• Build and iterate multi-component AI pipelines: RAG, fine-tuning and parameter-efficient tuning, embedding generation, hybrid retrieval, reasoning, tool use, function calling and orchestration
• Build agentic systems with clear separation of concerns, memory architecture and tool integration
• Contribute to model experimentation, evaluation design and production deployment, and maintain APIs, microservices, CI/CD pipelines and cloud-native deployments
• Help build GenAIOps for automated testing, regression evaluation, latency monitoring and continual improvement
• Balance performance, safety, responsible AI and cost: guardrails, fallbacks, red-teaming, human-in-the-loop workflows and automated evaluation suites
• Evaluate hallucination, relevance, latency, throughput, cost and efficiency, and partner with product, engineering, data science, ethics and infrastructure teams
• Communicate technical findings to stakeholders and clients, share knowledge and support technical learning around RAG, agents, prompt engineering and AI safety

Required profile

• 3-5+ years in software engineering, ML engineering or applied AI roles with hands-on building
• Demonstrated experience shipping generative AI features or systems end-to-end
• German at C1 level or higher
• Ability to explain technical concepts to non-technical stakeholders and to collaborate across engineering, product and data science teams
• Experience in client-facing consulting or enterprise transformation is a strong plus; mentoring peers is a plus

Required skills

• RAG architectures, vector and graph databases, and agentic patterns (tool use, routing, memory, multi-agent systems)
• Prompt and context engineering, ReAct, RLAIF and HITL feedback loops
• System design, architecture and production-grade deployment, with awareness of LLM cost and latency trade-offs
• Orchestration frameworks, model and API ecosystems, and evaluation design

The role requires at least three days per week in person, at a client site or Bain home office, with travel beyond the primary location depending on project needs.