Senior Manager, AI Engineering
Listed on 2026-03-03
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Data Scientist, Data Analyst
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.
You’ll join our Data Science & Machine Learning Engineering experts within the AI, Insights & Solutions team. This team is part of Bain’s digital capabilities practice, which includes experts in analytics, engineering, product management, and design. In this multidisciplinary environment, you'll leverage deep technical expertise with business acumen to help clients tackle their most transformative challenges. You’ll work on integrated teams alongside our general consultants and clients to develop data-driven strategies and innovative solutions.
Together, we create human-centric solutions that harness the power of data and artificial intelligence to drive competitive advantage for our clients. Our collaborative and supportive work environment fosters creativity and continuous learning, enabling us to consistently deliver exceptional results.
Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.
THE IMPACT YOU’LL HAVEBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.
As a Senior Manager, you will shape how machine learning, data science, and agentic AI are architected and deployed in complex enterprise environments — ensuring that solutions move beyond experimentation into sustained, scalable value creation.
WHERE YOU’LL FIT WITHIN THE TEAMAs a Senior Manager in the Data Science & MLE guild, you will lead the design and delivery of advanced machine learning and agentic AI systems across industries. You’ll combine hands-on technical depth with team leadership and client ownership — guiding multidisciplinary teams while remaining closely involved in architecture, model development, evaluation strategy, and production deployment.
This role sits at the intersection of classical ML, deep learning, GenAI, and emerging agentic systems, with accountability for translating business ambition into scalable, secure, production-grade AI solutions.
WHAT YOU’LL DO Lead enterprise AI and agentic system design- Architect and oversee delivery of GenAI and agentic AI applications, from POCs to MVPs to scaled production deployments.
- Design LLM-driven applications such as copilots, workflow automation tools, and decision-support systems integrated into enterprise environments.
- Implement robust agentic workflows including tool use, orchestration, routing, memory/state management, human-in-the-loop controls, and clear failure handling.
- Design advanced retrieval and knowledge systems (hybrid search, vector databases, knowledge graphs, metadata strategy, reranking, caching, and source attribution).
- Balance performance, reliability, latency, cost, security, and adoption in real-world enterprise settings.
- Oversee end-to-end ML lifecycle: data preparation, feature…
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