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 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 workplace. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.

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 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.

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.

The Role
As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.

The Role
The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients.



You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands‑on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

The Role
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

Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production‑grade AI applications.

Build and iterate on 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 reasoning, tool use, function calling, and orchestration across AI workflows

Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration

Contribute across the full stack: model experimentation, evaluation design, and production system deployment

Build and maintain APIs, microservices, CI/CD pipelines, and cloud‑native deployments with attention to observability and reliability

Support and help build 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 build robust, compliant AI systems

Contribute technical insights and communicate findings clearly to cross‑functional stakeholders and, where relevant, clients

Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety

What We’re Looking For

3–5+ years in software engineering, ML engineering, or applied AI roles with hands‑on building responsibilities

German language proficiency at C1 level or higher

Demonstrated experience shipping generative AI features or systems end‑to‑end, from prototyping through production

Clear communication skills with the ability to explain technical concepts to non‑technical collaborators and stakeholders

Demonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plus

Solid prompt engineering and context engineering skills; familiarity with conversation design principles

Working knowledge of evaluation design, experimentation frameworks, and data labelling strategies for LLM applications

Experience with:

RAG architectures (vector‑based retrieval; exposure to hybrid or graph‑based approaches is a plus)

Agentic patterns (tool use, routing, memory management; multi‑agent systems experience is a plus)

ReAct, RLAIF, and other HITL + feedback loops.

AI‑Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases, and Model + API ecosystems

Strong background in system design, architecture, and production‑grade deployment

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 technical leadership role and support junior team members

Experience in client‑facing consulting or enterprise transformation environments is a strong plus

Working Model & Travel

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.

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📌 Lead, AI Engineering (Ontario)
🏢 Bain
📍 Ontario