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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 Engineer, you will build the technical core of these transformations, moving solutions from prototype to adoption and achieving measurable, enterprise-level impact.

Responsibilities

Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
Build and iterate on multi-component AI pipelines, including Retrieval-Augmented Generation (RAG), fine-tuning, embedding generation, and hybrid retrieval strategies.
Implement reasoning, tool use, function calling, and orchestration across AI workflows.
Build and contribute to agentic systems, applying sound engineering principles.
Contribute across the full stack: model experimentation, evaluation design, and production system deployment.
Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliability.
Support and help build 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, factual consistency, relevance, latency, throughput, system-level performance, and cost efficiency.
Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems.
Contribute technical insights and communicate findings clearly to cross-functional stakeholders and clients.
Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety.

Requirements

3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilities.
Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production.
Clear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholders.
Demonstrated ability to collaborate effectively across engineering, product, and data science teams.
Solid prompt engineering and context engineering skills; familiarity with conversation design principles.
Working knowledge of evaluation design, experimentation frameworks, and data labelling strategies for LLM applications.
Experience with RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus).
Experience with agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus).
Experience with ReAct, RLAIF, and other HITL + feedback loops.
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.
Familiarity with cost and latency tradeoffs when working with LLM workloads.
Comfort operating in high-ambiguity environments with collaborative cross-functional teams.
Eagerness to grow into a technical leadership role and support junior team members.
Experience in client-facing consulting or enterprise transformation environments is a strong plus.

Skills

Generative AI
Multi-agent workflows
Production-grade AI applications
Retrieval-Augmented Generation (RAG)
Fine-tuning
Parameter-efficient tuning
Embedding generation and optimization
Hybrid retrieval strategies (vector, graph, keyword)
Reasoning
Tool use
Function calling
Orchestration
Agentic systems
Separation of concerns
Memory architecture
Tool integration
Model experimentation
Evaluation design
Production system deployment
APIs
Microservices
CI/CD pipelines
Cloud-native deployments
Observability
Reliability
GenAIOps
Automated testing
Regression evaluation
Latency monitoring
Continual improvement
Performance optimization
Safety
Responsible AI principles
Cost management
Guardrails
Fallbacks
Red-teaming strategies
Human-in-the-loop (HITL) workflows
Responsible AI standards
Automated evaluation suites
User signals
Continual learning cycles
Model updates
Evaluation frameworks
Hallucination rate
Factual consistency
Relevance
Precision/recall
Throughput
System-level performance
Cost tracking
Efficiency
Prompt engineering
Context engineering
Conversation design principles
Data labelling strategies for LLM applications
Vector-based retrieval
Hybrid retrieval
Graph-based retrieval
Tool use
Routing
Memory management
Multi-agent systems
ReAct
RLAIF
HITL + feedback loops
Orchestration frameworks
Vector databases
Graph databases
Model + API ecosystems
System design
Architecture
Production-grade deployment
LLM workloads
Client-facing consulting
Enterprise transformation

Location

Berlin
Munich
Zurich

Work Type

Permanent Full-Time
Hybrid

Experience Level

3-5+ years
Lead

About the Company

We are proud to be consistently recognized as one of the world’s best places to work.
We are currently the top-ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 spot a record seven times.
Extraordinary teams are at the heart of our business strategy.
We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience.
Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model.