Manager, AI Engineering
Bain & Company
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About Bain AI, Insights & Solutions (AIS)
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 Have
Bain 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 Manager, AI Engineering in AIS, you will lead the technical delivery of these transformations, bridging individual contribution and team leadership to move solutions from prototype to real adoption. You will take ownership of workstreams, guide more junior engineers, and interface with cross-functional partners and client stakeholders to ensure technical work lands with measurable enterprise impact.
The Role
The Manager, AI Engineering will design, build, and ship generative AI systems and agentic solutions for Bain's clients — while beginning to lead others in doing the same. This role sits at the inflection point between senior individual contributor and emerging technical leader. You will take full ownership of components and workstreams within larger programs, influence technical direction, and actively develop the engineers around you.
You will have opportunities to work with major AI ecosystem partners through Bain's partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.
Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.
What You'll Do
- Own the design, development, and deployment of generative AI systems and multi-agent workflows within client engagements, taking end-to-end accountability for assigned workstreams.
- Architect and build multi-component AI pipelines, including:
- Retrieval-Augmented Generation (RAG)
- Fine-tuning and parameter-efficient tuning
- Embedding generation and optimization
- Hybrid retrieval strategies (vector, graph, keyword)
- Implement and integrate reasoning, tool use, function calling, and orchestration across AI workflows.
- Build and lead delivery of agentic systems with clear separation of concerns, robust memory architecture, and scalable tool integration.
- Drive workstreams from research and experimentation through production deployment, maintaining quality and pace.
- Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with strong attention to observability and reliability.
- Establish and improve GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement.
- Balance performance, safety, responsible AI principles, and cost across system design:
- Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows
- Partner with global ethics teams to ensure alignment with Bain's Responsible AI standards
- Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates
- Design and implement evaluation frameworks covering:
- Hallucination rate and factual consistency
- Relevance and precision/recall
- Latency, throughput, and system-level performance
- Cost tracking and efficiency
- Partner closely with product, engineering, data science, ethics, and infrastructure teams to deliver robust, compliant AI systems.
- Communicate technical decisions and findings clearly to cross-functional stakeholders and, where relevant, clients; begin contributing to architecture discussions at the program level.
- Actively mentor and develop more junior engineers (Associates, Senior Associates, Leads), sharing knowledge on RAG, agents, prompt engineering, and AI safety best practices.
- Contribute to a culture of technical excellence, learning, and inclusion within the AIS engineering community.
- 6 -11+ years in software engineering, ML engineering, or applied AI roles, with significant hands-on building experience.
- Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production, with ownership of one or more full workstreams.
- Emerging leadership presence: a track record of stepping up to guide peers, coordinate workstreams, and drive technical decisions within a team.
- Strong communication skills — able to explain complex technical concepts clearly to non-technical collaborators and client stakeholders.
- Solid prompt engineering and context engineering skills; familiarity with conversation design principles.
- Strong working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications.
- Solid experience with:
- RAG architectures (vector-based retrieval; hybrid or graph-based approaches a plus)
- Agentic patterns (tool use, routing, memory management; multi-agent systems a plus)
- ReAct, RLAIF, and other HITL + feedback loop methodologies
- AI-specific tools & frameworks — orchestration frameworks, vector and graph databases, model + API ecosystems
- Strong background in system design, architecture, and production-grade deployment.
- Solid familiarity with cost and latency tradeoffs when working with LLM workloads.
- Comfort operating in high-ambiguity environments with collaborative cross-functional teams.
- Eagerness to grow into a full technical leadership role; a demonstrated appetite for developing others.
- Experience in client-facing consulting or enterprise transformation environments is a strong plus.
- This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.
- Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.