Senior AI/ML Engineering Manager

Description & Requirements

WHAT MAKES US A GREAT PLACE TO WORK We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally. WHO YOU’LL WORK WITH As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning. Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses. WHERE YOU’LL FIT WITHIN THE TEAM Senior AI/ML Engineering Managers lead the Data and ML Engineering function for the Diligence Platform: they own the people, delivery, and technical direction of the teams that build feature and embedding pipelines, model serving and LLMOps infrastructure, and production RAG and retrieval systems. You manage and develop a team of Data and ML Engineers — hiring, coaching, performance management, and career growth — while owning the roadmap, delivery commitments, and operational health of everything the team ships. You remain hands-on where it matters most: setting architectural direction, prototyping hard problems, reviewing critical code, and stepping into individual contribution when delivery, incidents, or complexity demand it. You partner with Data Science, the Agent / AI squad, Product, and Platform leadership to translate platform strategy into staffed, sequenced, and measurable engineering work. This is a player-coach role: the team’s output, reliability, and engineering standards are the measure of success.

Team Leadership and Engineering Management (50% ) Manage a team of Data and ML Engineers: hiring and onboarding, goal setting, coaching, performance management, and career development. Own delivery for the team’s roadmap: scope and sequence work, set realistic commitments, track progress, and remove blockers before they become escalations. Set and enforce engineering standards across data and ML systems — testing, typing, observability, runbooks, and SLAs — and hold the team to production quality from the first commit. Own the operational health of the team’s systems: on-call and incident response ownership, post-incident reviews, and follow-through on reliability and cost improvements. Allocate capacity across pipeline, serving, retrieval, and evaluation workstreams; balance feature delivery against platform and technical-debt investment. Partner with Data Science, the Agent / AI squad, Product, and Platform leadership to translate platform strategy into staffed, sequenced engineering work with clear ownership. Grow senior engineers into technical leaders: delegate architectural ownership, coach through design review, and build bench depth behind every critical system. Communicate roadmap, trade-offs, risks, and delivery status to platform leadership