Experteer Overview
In this role you design, build, and deploy generative AI systems for Bain AIS, moving from prototype to production within cross-functional teams. You translate ambiguous business problems into scalable AI applications that deliver measurable client impact. You contribute to technical direction, mentor juniors, and collaborate with ecosystem partners to apply AI responsibly at enterprise scale. This is a hands-on, growth-oriented role with a clear impact on client outcomes and internal tech leadership.

Compensaciones / Incentivos
• Design, develop, and deploy end-to-end generative AI systems, including multi-agent workflows
• Build and refine multi-component AI pipelines (RAG, embedding, fine-tuning, hybrid retrieval)
• Implement reasoning, tool use, function calling, and orchestration across AI workflows
• Develop agentic systems with clean architecture, memory management, and tool integration
• Contribute across full stack: experimentation, evaluation, and production deployment
• Maintain APIs, microservices, CI/CD, and cloud-native deployments with observability
• Support GenAIOps for automated testing, latency monitoring, and continual improvement
• Balance performance, safety, and cost; implement guardrails and HITL workflows
• Partner with ethics teams to align with Responsible AI standards
• Create evaluation suites for hallucination, relevance, latency,



and cost
• Collaborate with product, engineering, data science, ethics, and infrastructure; share knowledge

Responsabilidades
• 3-5+ years in software/ML engineering or applied AI with hands-on building responsibilities
• Experience shipping generative AI features end-to-end, from prototype to production
• Strong communication skills to explain technical concepts to non-technical stakeholders
• Proven ability to collaborate across engineering, product, and data science; mentoring is a plus
• Solid prompt and context engineering skills; understanding of conversation design
• Knowledge of evaluation design, experimentation frameworks, and data labeling for LLMs
• Experience with RAG architectures (vector retrieval; graph-based approaches a plus) and agentic patterns (tool use, memory management)
• Familiarity with ReAct, RLAIF, and HITL + feedback loops; AI-specific tools u0026 frameworks for orchestration, vector/graph databases, and model+API ecosystems
• Strong system design, production-grade deployment, and awareness of cost/latency tradeoffs in LLM workloads
• Comfort in high-ambiguity environments; interest in technical leadership and mentoring
• Prior client-facing consulting or enterprise transformation experience is a plus

Requisitos principales
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📌 Lead, AI Engineering (Madrid)
🏢 Bain u0026
📍 Madrid