Lead AI Engineer (Generative AI)

Location: São Paulo, Brazil; hybrid work with a minimum of three days per week in person at a client location or Bain home office. Travel beyond the primary working location is required, with frequency and destination varying by project needs.

Company

Bain & Company’s AI, Insights & Solutions team designs and delivers AI-powered solutions for clients, combining technology with redesigned processes, operating model changes, and adoption at scale.

What you will do

• Design, build, and deploy end-to-end generative AI systems, multi-agent workflows, and production-grade AI applications.
• Develop RAG, fine-tuning, embedding, hybrid retrieval, reasoning, tool-use, function-calling, and orchestration capabilities.
• Work across model experimentation, evaluation design, APIs, microservices, CI/CD, cloud-native deployment, observability, and reliability.
• Build GenAIOps and automated evaluation systems covering hallucination, factual consistency, relevance, precision/recall, latency, throughput, and cost.
• Implement guardrails, fallbacks, red-teaming, human-in-the-loop workflows, and responsible AI practices.
• Collaborate with product, engineering, data science, ethics, infrastructure, and client stakeholders; share technical knowledge and support junior team members.

Requirements

• 3–5+ years of hands-on experience in software engineering, ML engineering, or applied AI roles, including experience shipping generative AI systems from prototype to production.
• Experience with RAG architectures, vector retrieval, agentic patterns, tool use, routing, memory management, ReAct, RLAIF, HITL feedback loops, orchestration frameworks, vector and graph databases, and model/API ecosystems.
• Solid prompt engineering, context engineering, and conversation design skills.
• Working knowledge of evaluation design, experimentation frameworks, and data-labeling strategies for LLM applications.
• Strong system design, architecture, and production deployment background, with familiarity with LLM cost and latency trade-offs.
• Ability to communicate technical concepts clearly to non-technical stakeholders and collaborate across engineering, product, and data science teams.
• Comfort working in high-ambiguity, cross-functional environments and eagerness to grow into technical leadership while supporting junior team members.

Nice to have

• Experience with hybrid or graph-based retrieval and multi-agent systems.
• Experience supporting or informally mentoring peers.
• Experience in client-facing consulting or enterprise transformation environments.

Culture & Benefits

• Work in multidisciplinary teams with Bain consultants, product, design, architecture, engineering, and client stakeholders.
• Apprenticeship-based learning with experienced teammates, frequent feedback, and increasing responsibility.
• Opportunities to work with major AI ecosystem partners on real client deployments.
• Permanent full-time employment with a hybrid in-person model and project-based travel.