Director, AI and Machine Learning Engineering

Bain & Company • Boston, MA

2d • $225,750 - $282,500

About The Position

The Director, AI and Machine Learning Engineering will build and own the shared Agentic Core and interface infrastructure for Bain’s Growth AI Performance System, a suite of tools supporting commercial decisions across marketing ROI, brand, and pricing strategy. The role develops the central intelligence layer—including agentic workflows, retrieval and knowledge pipelines, tool integrations, and user-facing infrastructure—that all product modules depend on. It takes AI applications from early proofs of concept and MVPs through scaled production deployments, with responsibility for the reliability, quality, and coherence of systems used by client teams.

The position combines research and production engineering, applying judgment to determine where agentic approaches are useful, how systems can fail gracefully, and how to measure quality for real users. The Director works closely with consulting teams, product, engineering, data science, ethics, and infrastructure colleagues. Direct engagement with consulting teams and senior stakeholders informs engineering improvements; the role also communicates technical designs and tradeoffs in business terms and mentors engineers and case team members.

The position is part of a standing product team building enterprise AI software. It involves production-grade agentic systems, real-time decision engines, cloud-native infrastructure, and complex enterprise data. The role calls for strong technical expertise, client awareness, and ongoing learning as AI capabilities evolve.

Requirements

• Strong proficiency in Python and experience building and integrating production services.
• Hands-on experience with LLM APIs, agentic orchestration frameworks, and deploying services on Azure or AWS.
• Experience designing and implementing agentic workflows and multi-step LLM pipelines, including context engineering, memory and state management, tool integration, routing, planning and reflection, and orchestration patterns.
• Working knowledge of ReAct, RLAIF, and other human-in-the-loop and feedback-loop techniques.
• Experience building retrieval and knowledge pipelines using vector, keyword, hybrid, and graph-based data structures, with hands-on use of vector and graph databases.
• Understanding of embedding optimization, indexing strategies, relevance tuning, and retrieval integration with LLM-powered workflows.
• Proficiency in MLOps and AIOps practices, including CI/CD for AI services, versioning for models, prompts, and agents, automated evaluation pipelines, and observability for agentic systems.
• Advanced prompt engineering, context engineering, and conversation design; strong grounding in evaluation design, experimentation frameworks, and data-labeling strategies for LLM applications.
• Solid system design and architecture skills, including familiarity with cost optimization and computational tradeoffs for LLM workloads.
• Ability to apply data science and machine learning skills to improve system performance, including feature engineering, model selection, training, validation, and reproducible evaluation.
• Strong engineering fundamentals, including clean and maintainable code, code review, version control, automated testing, and performance profiling.
• Active use of AI-assisted coding tools such as Cursor, Claude Code, or equivalent in day-to-day engineering.
• Demonstrated commitment to responsible AI practices, including access controls, auditability, bias awareness, and sound deployment judgment.
• 10 or more years in software engineering or applied AI roles with significant hands-on building responsibilities.
• Track record of shipping production AI or ML systems end-to-end, from experimentation through live deployment.
• Experience contributing to complex, multi-stack generative AI programs from conception through production, including mentoring engineers.
• Experience in agile, cross-functional product or consulting-adjacent environments; strong troubleshooting and issue-resolution skills; comfort working in high-ambiguity environments.
• Associate’s degree or bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or an equivalent combination of education, training, and experience.

Nice To Haves

• Direct experience engaging with clients or senior business stakeholders in a technical capacity—presenting, advising, or shaping solutions in consulting-adjacent environments—is a strong advantage.

Responsibilities

• Build and own core components of the Growth AI Performance System Agentic Core, including multi-agent orchestration frameworks, tool registries, shared memory and state management, and interface infrastructure.
• Develop enterprise AI applications across the delivery lifecycle, from proofs of concept and MVPs to scaled production deployments; build evaluations to measure output quality, benchmark models, and validate reliability.
• Implement agentic capabilities and patterns, including context engineering, memory and state management, reasoning and planning, reflection, orchestration, routing, tool selection, function calling, and tool integration.
• Design and build retrieval and knowledge pipelines across vector, keyword, hybrid, and graph-based data, including embedding generation and optimization, indexing, metadata, relevance tuning, caching, access controls, and source attribution.
• Apply fine-tuning and parameter-efficient tuning when they measurably outperform prompting and retrieval; conduct model experimentation and early-stage research to inform architecture decisions.
• Integrate platform capabilities into enterprise environments while balancing quality, latency, cost, privacy, and adoption; create reusable components and accelerators for the product modules.
• Write clean, testable, maintainable code; build APIs and microservices for cloud-native deployment; implement MLOps, AIOps, and GenAIOps practices, including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents.
• Build evaluation and observability for the Agentic Core, including tracing and instrumentation, regression tests, automated scoring pipelines, and iteration loops for prompt and policy optimization.
• Design evaluation frameworks tracking hallucination and factual consistency, relevance and retrieval precision/recall, latency and throughput, and cost per task; use user signals and human feedback for continual improvement and model updates.
• Apply data science and machine learning methods to improve platform performance or reliability, including data preparation, feature engineering, model selection, training, validation, and reproducible evaluation.
• Design secure enterprise deployments with access controls, auditability, sensitive-data safeguards, responsible AI guardrails, fallbacks, red-teaming, and human-in-the-loop review; implement security, privacy, and governance requirements and partner with Bain’s Responsible AI and ethics teams.
• Engage consulting teams and senior stakeholders to understand platform usage and shortcomings, and translate feedback into engineering improvements.
• Present agentic system designs, tradeoffs, and implementation decisions to senior stakeholders, including C-suite and executive leadership, in clear non-technical terms.
• Contribute to discovery sessions and solution shaping with case teams; connect engineering decisions to commercial outcomes and advise on AI strategy, architecture, emerging capabilities, and implementation roadmaps.
• Participate in proofs of concept and innovation workstreams, stay current with AI and agentic trends, and share learnings across the team.
• Partner with product, engineering, data science, ethics, and infrastructure colleagues to build robust, compliant AI systems.
• Mentor and upskill engineers and case team members in RAG, agents, prompt engineering, and AI safety.

Benefits

• Annual discretionary performance bonus and potential eligibility for other discretionary compensation.
• 401(k) plan with an annual employer contribution; the stated contribution is 4.5%, increases after three years of service, and is fully vested upon start date.
• Bain pays 100% of individual employee premiums for medical, dental, and vision programs.
• Generous paid time off, including parental leave, sick leave, and paid holidays.
• Paid life and long-term disability insurance.
• Comprehensive benefits and wellness program.

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: Associate degree