Lead, AI Engineering

While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed, including building evaluation approaches, working with data pipelines, and developing or integrating ML models when they materially improve performance or reliability.

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

Build AI applications that drive real business outcomes

Design and develop GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.

Implement agentic workflows where they add clear value (e.g., tool use, multi-step execution, human-in-the-loop controls), with attention to reliability, safety, and clear failure modes.

Design and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores (e.g., hybrid search, vector stores, graph databases/knowledge graphs, and traditional data platforms), covering indexing strategies, metadata design, relevance tuning/reranking, freshness, caching, access controls, and source attribution.

Build robust agent capabilities including context engineering, memory/state management (short-term and long-term), orchestration, routing, and tool integration patterns.

Integrate solutions into enterprise environments and workflows (APIs, data systems, collaboration tools), balancing quality, latency, cost, privacy, and adoption.

Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.

Build and apply data science and machine learning capabilities

Build ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/testing, and performance analysis.

Apply the right methods for the problem, spanning classical ML and deep learning (including sequence, text, and image models when relevant).

Create reproducible training and evaluation pipelines (versioning, experiment tracking, robust validation, clear documentation).

Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training vs post-training concepts (e.g., instruction tuning and preference optimization approaches).

Engineer for real delivery: POC → MVP → production

Write clean, testable, maintainable code and ship AI services through the full SDLC: build → test → deploy → monitor → iterate.

Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.

Build evaluation and observability for GenAI and agentic systems: tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt/policy optimization.

Design for secure enterprise deployment: access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.

Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.

Thrive in a client-facing consulting environment

Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and write crisp technical documentation.

Work effectively with Bain consultants to prioritize the critical few technical decisions that unlock business value.

Support proposal shaping and scoping: effort sizing, architecture options, risk assessment, and delivery roadmaps.

What We’re Looking For (Qualifications)

Core engineering + AI application skills

3–5+ years of professional AI / ML engineering experience (or equivalent), with strong backend engineering fundamentals.

Strong proficiency in Python and experience building APIs/services (e.g., REST/gRPC) and integrating with enterprise systems.

Hands-on experience building LLM-powered applications with delivery considerations (latency, cost, reliability, security).

Experience building advanced retrieval/search systems (e.g., hybrid retrieval, vector search, reranking), and comfort working across multiple data stores (vector, graph, relational/document/search).

Experience implementing agentic patterns (context management, tool integration, orchestration, and memory/state handling) and strong judgment about when agentic approaches are (and are not) appropriate.

Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.

Cloud, platform, and production delivery experience

Experience deploying and operating services on AWS, GCP, and/or Azure (environment management, reliability, observability, scaling).

Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production (debugging, performance, resilience).

Proven ability to implement security, privacy, and governance requirements for AI systems (authentication/authorization, access controls, PII/sensitive data handling, enterprise risk controls).

Breadth of knowledge across data science and machine learning

Experience training, validating, and testing ML models; strong understanding of overfitting, generalization, and evaluation methodology.

Practical experience with feature engineering and data preprocessing for real-world datasets.

Familiarity with a broad set of ML algorithms (classical ML and deep learning), and the ability to choose methods that match the business and data constraints.

Familiarity with deep learning frameworks (e.g., PyTorch/TensorFlow) and ML lifecycle tooling (e.g., experiment tracking, model registry, feature store concepts).

Delivery mindset and consulting skills

Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a collaborative team.

Excellent interpersonal and communication skills, able to explain technical decisions, tradeoffs, and results to mixed audiences.

Strong stakeholder management skills; comfort working directly with clients.

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.

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 be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

In New York City, California, Washington State, and Washington D.C, the good-faith, reasonable annualized full-time salary for this role is $203,500; In the state of Illinois, Georgia, Massachusetts, and Texas, the good-faith, reasonable annualized full-time salary for this role is $179,500.

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.

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

Annual fitness reimbursements

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