Overview

What Makes Us a Great Place to Work

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
• 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 everything from early-stage research, model experimentation, and evaluation design to 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:
• Implement 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
• 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

Qualifications

• 8-12+ years in software engineering, ML engineering, or applied AI roles with significant hands-on building responsibilities
• 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)
• Agentic architectures (multi-agent systems, tool selection, routing, memory, planning, reflection)
• 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
• 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
• 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.
• https://www.bain.com/about/awards-recognition/
• https://www.bain.com/careers/work-with-us/integrated-teams/ai-insights-solutions-ais/