Director, AI and Machine Learning Engineering

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Who You’ll Work With

You will be part of a team building Bain's integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.

You will work inside Bain's standing product team, building software that helps the world’s largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.

The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.

This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain’s global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.

We are looking for engineers who see how to make a product better and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves real-time decision engines, cloud-native infrastructure, and data problems with no off-the-shelf answer. Experience with agentic systems matters: tool and context protocols such as MCP, evaluation harnesses, memory design, and entity resolution across messy enterprise data. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.

Where You’ll Fit Within the Team

The AI Engineer is a core builder of the Growth AI Performance System platform’s Agentic Core and Interface: the shared foundation that all three product modules are built on. This is not a module-level role; you are building the central intelligence layer: the agentic workflows, retrieval and knowledge pipelines, tool integrations, and interface infrastructure that every product in the suite depends on. Moving solutions from early POC and MVP through to scaled production deployments, you are responsible for the reliability, quality, and coherence of the systems that real client teams use every day.

You will work across the platform and product modules focused on marketing ROI, brand, and pricing strategy, translating the analytical and commercial questions that client teams are trying to answer into reliable, production-grade agentic systems that deliver those answers at scale.

The role requires both deep technical capability and genuine client awareness. You will engage directly with consulting teams and, at times, with senior stakeholders, presenting how the Agentic Core works, translating system design decisions into business terms, and using that feedback to make the platform better. The build loop here is tight: client engagement informs engineering, and every release makes the suite more capable.

This role sits at the intersection of research and production engineering. The problems are rarely solved by off-the-shelf tooling: they require judgment about when agentic approaches add real value, how to build systems that fail gracefully, and how to measure quality in ways that matter to real users on real client engagements.

What You’ll Do

Building the Agentic Core

• Build and own core components of the Growth AI Performance System Agentic Core, the shared intelligence layer that all product modules depend on, including multi-agent orchestration frameworks, tool registries, shared memory and state management, and the interface infrastructure that surfaces platform capabilities to users
• Build AI applications for enterprise use cases across the full delivery lifecycle, from rapid POCs and MVPs through to scaled production deployments, designing AI workflows that execute multi-step tasks reliably and safely – building evals to measure output quality, benchmark model performance, and validate reliability before and after deployment
• Implement agentic patterns with strong judgment about when they add real value; build robust agent capabilities including context engineering, memory and state management, reasoning and planning, reflection, orchestration, routing, tool selection, function calling, and tool integration, with clear separation of concerns so the tool ecosystem scales cleanly
• Design and build advanced retrieval and knowledge pipelines across diverse data structures (vector, keyword, hybrid, and graph-based), covering embedding generation and optimization, indexing strategies, metadata design, relevance tuning, caching, access controls, and source attribution
• Apply fine-tuning and parameter-efficient tuning techniques where they measurably outperform prompting and retrieval alone; run model experimentation and early-stage research to inform architecture decisions
• Integrate platform capabilities into enterprise environments, balancing quality, latency, cost, privacy, and adoption; build reusable components and accelerators that scale across all three product modules

Engineering quality and operations

• Engineer for real delivery across the full software development lifecycle: 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: tracing and instrumentation of agentic execution, regression test suites, automated scoring pipelines, and iteration loops for prompt and policy optimization
• Design evaluation frameworks that track hallucination rate and factual consistency, relevance and retrieval precision/recall, latency and throughput, and cost per task; feed user signals and human feedback into continual improvement cycles and ongoing model updates
• Apply data science and ML skills as needed to materially improve platform performance or reliability: data preparation, feature engineering, model selection, training, validation, and reproducible evaluation pipelines
• Design for secure enterprise deployment: access controls, auditability, responsible handling of sensitive data, and responsible AI guardrails, including fallbacks, red‑teaming, and human-in-the-loop (HITL) review; implement security, privacy, and governance requirements across all platform services; partner with Bain’s global Responsible AI and ethics teams to keep the platform aligned with firm standards

Internal stakeholder communication skills

• Engage directly with consulting teams and senior stakeholders to understand how the Agentic Core is being used, where it falls short, and what needs to change; translate that feedback into concrete engineering improvements
• Present complex agentic system design, tradeoffs, and implementation decisions to senior stakeholders, including C-suite and executive leadership, in clear, non-technical terms that build confidence in the platform
• Contribute to discovery sessions and solution shaping with case teams; operate as a credible, composed technical voice that connects engineering decisions to commercial outcomes; act as a thought partner on AI strategy, architecture choices, emerging capabilities, and implementation roadmaps
• Drive innovation through out-of-the-box thinking; participate in POCs and innovation workstreams to validate new tools and technologies; stay current with emerging AI and agentic trends and share learnings across the team
• Partner closely with product, engineering, data science, ethics, and infrastructure colleagues to build robust, compliant AI systems
• Mentor and upskill engineers and case team members on best practices in RAG, agents, prompt engineering, and AI safety

About You

Technical

• 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; deep familiarity with context engineering, memory and state management, tool integration, routing logic, planning and reflection, and orchestration patterns; working knowledge of ReAct, RLAIF, and other HITL and feedback‑loop techniques
• Experience building retrieval and knowledge pipelines across vector, keyword, hybrid, and graph-based data structures, including hands‑on use of vector and graph databases; understanding of embedding optimization, indexing strategies, relevance tuning, and how retrieval connects to LLM-powered workflows
• Proficiency with MLOps and AIOps practices: 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, with deep familiarity with cost optimization and computational tradeoffs for LLM workloads
• Ability to apply data science and ML skills to improve system performance: feature engineering, model selection, training, validation, and reproducible evaluation pipelines
• Strong engineering fundamentals: clean, testable, maintainable code; code review; version control; automated testing; and performance profiling
• Native AI fluency in day‑to‑day engineering, active use of AI‑assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development and prototype faster
• Demonstrated commitment to responsible AI practices: access controls, auditability, bias awareness, and the judgment to know when a system is not ready to deploy

Experience

• 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 early experimentation through to live deployment
• Experience contributing to complex, multi‑stack generative AI programs from conception through production, including mentoring other engineers along the way
• Experience working in agile, cross‑functional product or consulting‑adjacent environments; strong troubleshooting and issue resolution skills; comfortable operating in high‑ambiguity environments
• Direct experience engaging with clients or senior business stakeholders in a technical capacity, presenting, advising, or shaping solutions in a consulting‑adjacent environments is a strong advantage

Education

Associate’s degree / Bachelor’s degree in computer science, engineering, mathematics, or a related technical field; or an equivalent combination of education, training, and experience

U.S. Compensation Information

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).

Some local governments in the United States require a good‑faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

• In Massachusetts, the good‑faith, reasonable annualized full‑time salary range for this role is between $235,750-$282,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
• In Illinois, the good‑faith, reasonable annualized full‑time salary range for this role is between $225,750-$270,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
• Annual discretionary performance bonus
• This role may also be eligible for other elements of discretionary compensation
• 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date

Bain & Company’s comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.

• Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
• Generous paid time off, including parental leave, sick leave and paid holidays
• Fully vested 401(k) company contribution
• Paid Life and Long-Term Disability insurance

For all other locations, the good‑faith, reasonable annualized full‑time salary range for this role is commensurate with competitive geographic market rates for this role and will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.