We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into r obust AI applications that can be piloted, scaled, and adopted.
The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.
The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.
You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.
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. Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
Hybrid retrieval strategies (vector, graph, keyword)
Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration
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:

Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows
Partner with integral ethics teams to ensure alignment with Bain’s Responsible AI standards
Latency, throughput, and system-level performance
Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems
Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety
3–5+ yearsin software engineering, ML engineering, or applied AI roles with hands-on building responsibilities
~ Demonstrated ability to collaborate effectively across engineering, product, and data science teams; Solid prompt engineering and context engineering skills; Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications
~ exposure to hybrid or graph-based approaches is a plus)
~ ReAct, RLAIF, and other HITL + feedback loops.
~ AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems

Strong background in system design, architecture, and production-grade deployment
Eagerness to grow into a technical leadership role and support junior team members
This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.
Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.
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📌 Product Manager Engineering (m/f) (Madrid)
🏢 Bain
📍 Madrid