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About the Role

Bain's AI, Insights & Solutions (AIS) team designs and delivers AI-powered solutions for clients, creating measurable business impact. As an AI Engineering leader, you will build the technical core of these transformations, moving solutions from prototype to adoption and delivering enterprise-level impact. You will architect, build, and scale next-generation generative AI systems and agentic solutions, operating at the intersection of advanced engineering, applied AI research, product strategy, and responsible AI governance.

Responsibilities

Design, build, and deploy end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
Architect multi-component pipelines, including Retrieval-Augmented Generation (RAG), fine-tuning and parameter-efficient tuning, embedding generation and optimization, and hybrid retrieval strategies (vector, graph, keyword).
Integrate reasoning, tool use, function calling, and orchestration across complex workflows.
Engineer advanced agentic systems, ensuring clear separation of concerns, robust memory architecture, and scalable tool ecosystems.
Lead research, model experimentation, evaluation design, and production system deployment.
Oversee API development, microservices, CI/CD pipelines, observability, and cloud-native deployment.
Build scalable GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement.
Balance performance, safety, responsible AI principles, and cost across system design, implementing guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows.
Partner with global ethics teams to ensure alignment with Bain’s Responsible AI standards.
Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates.
Design and implement evaluation frameworks covering hallucination rate and factual consistency, relevance and precision/recall, latency, throughput, and system-level performance, and cost tracking and efficiency.
Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems.
Act as a thought partner to executives and clients on AI strategy, architecture decisions, emerging capabilities, and implementation roadmaps.
Mentor and upskill technical teams on best practices including RAG, agents, prompt engineering, and AI safety.

Requirements

8–12+ years in software engineering, ML engineering, or applied AI roles with significant hands-on building responsibilities.
German language proficiency at C1 level or higher.
Demonstrated experience leading complex, multi-stack generative AI programs from conception through production.
Strong executive communication skills with the ability to translate highly technical concepts to business stakeholders.
Track record of leading engineering teams, mentoring technical talent, and collaborating with diverse cross-functional groups.
Advanced prompt engineering, context engineering, and conversation design.
Strong expertise in evaluation design, experimentation frameworks, and data labeling strategies for LLM apps.
Deep experience with advanced RAG architectures (vector, hybrid, graph-based retrieval).
Deep experience with agentic architectures (multi-agent systems, tool selection, routing, memory, planning, reflection).
Deep experience with ReAct, RLAIF, and other HITL + feedback loops.
Deep experience with AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems.
Strong background in system design, architecture, and production-grade deployment.
Deep familiarity with cost optimization and computational tradeoffs for LLM workloads.
Comfort operating in high-ambiguity environments with collaborative cross-functional teams.
Clear ability to lead, mentor, and inspire technical teams.
Experience in client-facing consulting or enterprise transformation environments is a strong plus.

Skills

Generative AI
Agentic solutions
AI Engineering
Applied AI research
Product strategy
Responsible AI governance
Retrieval-Augmented Generation (RAG)
Fine-tuning
Parameter-efficient tuning
Embedding generation
Embedding optimization
Hybrid retrieval strategies
Vector retrieval
Graph retrieval
Keyword retrieval
Reasoning
Tool use
Function calling
Orchestration
Agentic systems
System design
Architecture
Production deployment
API development
Microservices
CI/CD pipelines
Observability
Cloud-native deployment
GenAIOps
Automated testing
Regression evaluation
Latency monitoring
Continual improvement
Performance optimization
Safety
Responsible AI principles
Cost optimization
Guardrails
Fallbacks
Red-teaming
Human-in-the-loop (HITL)
Evaluation design
Experimentation frameworks
Data labeling
LLM apps
Multi-agent systems
ReAct
RLAIF
Feedback loops
Orchestration frameworks
Vector databases
Graph databases
Model ecosystems
API ecosystems
Prompt engineering
Context engineering
Conversation design
AI strategy
Executive communication

Location

Berlin
Munich
Zurich

Work Type

Permanent Full-Time
Hybrid

Experience Level

Expert Senior Manager
Associate Partner
8-12+ years of experience

About the Company

Proud to be consistently recognized as one of the world’s best places to work.
Top ranked consulting firm on Glassdoor’s Best Places to Work list, earning the #1 overall spot a record seven times.
Focuses on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment.
Hires people with exceptional talent and creates an environment in which every individual can thrive professionally and personally.
Works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience.
Offers significant learning and growth opportunities through the breadth and depth of problems solved, the level of impact achieved, and an apprenticeship model.
Has major AI ecosystem partners through Bain’s partnerships.