Lead AI Engineering, Agentic Core and Interface

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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 contributes to building and shipping core components of the Growth AI Performance System platform’s Agentic Core and Interface: the shared foundation that all three product modules are built on. Working under the direction of senior engineers and the Tech Lead, you will implement agentic workflows, retrieval pipelines, and interface components, taking ownership of specific features and modules as you develop. Your work directly affects the quality and reliability 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 solid technical capability and a genuine curiosity about how AI systems work end-to-end. You will have exposure to consulting teams and, over time, to client stakeholders, developing the communication skills and commercial awareness that distinguish a strong mid-level engineer in a client-adjacent product environment.

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 ship components of the Growth AI Performance System Agentic Core (including agentic workflows, tool integrations, shared memory and state management, and interface components), under the direction of senior engineers and the Tech Lead
• Build GenAI and agentic AI applications for enterprise use cases across the delivery lifecycle, from POC and MVP through to production, implementing LLM-driven workflows that execute multi-step tasks reliably and safely
• Implement agentic patterns with sound judgment about when they add real value; build agent capabilities including context engineering, tool integration, orchestration, and routing logic with attention to reliability and clear failure modes
• Contribute to retrieval and knowledge pipelines (vector, hybrid, and graph-based), implementing indexing strategies, relevance tuning, and retrieval-to-LLM integration under architectural guidance
• Integrate platform capabilities into enterprise environments, balancing quality, latency, cost, and reliability; contribute to reusable components and accelerators shared across product modules

Engineering quality and operations

• Write clean, testable, maintainable code across the full software development lifecycle; implement MLOps and GenAIOps practices including CI/CD, versioning for models, prompts, and agents, and environment parity
• Contribute to evaluation and observability for the Agentic Core: implementing tracing and instrumentation, running regression tests, and supporting automated scoring pipelines
• Apply data science and ML skills to improve platform performance or reliability: feature engineering, model selection, validation, and reproducible evaluation pipelines
• Implement security, privacy, and governance requirements for AI systems; follow responsible AI practices and apply access controls and auditability standards across platform services

Internal stakeholder communication skills

• Engage with consulting teams to understand how the Agentic Core is being used in practice; surface that feedback to senior engineers and incorporate it into engineering improvements
• Develop the ability to communicate technical concepts clearly to non-technical stakeholders; with support from senior engineers, contribute to sessions where platform capabilities are explained to client teams
• Contribute constructively in cross-functional settings (working across engineering, data science, and product), and develop the commercial awareness that comes from building in a client-facing product environment
• Participate in technical discovery and POCs; stay current with emerging AI and agentic technologies and contribute to knowledge sharing across the team

About You

Technical

• Solid proficiency in Python and experience building and integrating services; working knowledge of LLM APIs and experience deploying services on Azure or AWS
• Working experience with agentic workflows and multi-step LLM pipelines; familiarity with context engineering, tool integration, orchestration patterns, and basic memory and state management
• Familiarity with retrieval and knowledge pipelines across vector or hybrid data structures; understanding of how retrieval connects to LLM-powered workflows
• Working knowledge of MLOps and GenAIOps practices: CI/CD for AI services, versioning for models and prompts, basic evaluation pipelines, and service observability
• Ability to apply data science and ML skills to improve system performance; comfort with feature engineering, model selection, and validation at a working level
• Strong engineering fundamentals: clean, testable code; code review; version control; automated testing; and debugging distributed systems
• Active use of AI-assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development and prototype faster
• Awareness of responsible AI practices: access controls, auditability, and an understanding of when a system may not be ready to deploy

Experience

• 5 or more years in software engineering or applied AI roles with hands-on building responsibilities
• Experience contributing to production AI or ML systems, from experimentation through to live deployment, with support from senior engineers
• Experience working in agile, cross-functional teams; strong troubleshooting and issue resolution skills
• Exposure to or strong appetite for client-facing or consulting-adjacent environments is an 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 $171,000 – $205,000; 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 $163,500 – $196,000; 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.