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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 an ML-AI Engineer Intern 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

We are hiring an ML-AI Engineer Intern to help build GenAI and agentic AI applications for enterprise use cases, ranging from rapid proofs of concept (POCs) to MVPs and, where appropriate, scaled production deployments. You will design and implement LLM-driven applications and agentic workflows that use tools, data, and enterprise systems to execute multi-step tasks reliably and safely.

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

Contribute to the design and development of GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.
Support the implementation of 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.
Assist in building robust agent capabilities including context engineering, memory/state management (short-term and long-term), orchestration, routing, and tool integration patterns.

Build And Apply Data Science And Machine Learning Capabilities

Contribute to ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/testing, and performance analysis.
Apply appropriate ML methods for the problem, spanning classical ML and deep learning (including sequence, text, and image models when relevant).
Develop working knowledge of modern deep learning concepts, including transformer fundamentals and LLM pre-training vs post-training concepts (e.g., instruction tuning, preference optimization).

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.
Support implementation of MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.
Contribute to 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.
Gain exposure to secure enterprise deployment practices, including access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.
Contribute to reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.

Thrive in a consulting environment

Communicate clearly with technical and non-technical audiences; present in working sessions, share recommendations, and help write crisp technical documentation.
Support client discussions, demos, and documentation development as part of broader engagement teams.

What We’re Looking For (Qualifications)

Educational background

Currently pursuing or recently completed a bachelor’s, master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, or a related quantitative field; or equivalent early-career industry experience.
Strong academic performance or demonstrated excellence through research, projects, internships, competitions, or open-source contributions.

Core engineering + AI application skills

0–2+ years of professional AI / ML engineering experience or equivalent experience through internships, research, academic projects, or industry roles, with strong backend engineering fundamentals.
Proficiency in Python and some experience building APIs/services (e.g., REST/gRPC) or comparable project-based experience.
Hands-on experience (academic, internship, or industry) building ML or LLM-powered applications, preferably with exposure to delivery considerations such as latency, cost, reliability, or security.
Exposure to retrieval/search systems (e.g., hybrid retrieval, vector search, reranking), and familiarity working with structured and unstructured data stores.
Exposure to agentic patterns (context management, tool integration, orchestration, memory/state handling); hands-on experience (academic, internship, or industry) preferred.
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.

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

Delivery Mindset And Consulting Skills

Excellent interpersonal and communication skills, able to explain technical decisions, tradeoffs, and results to mixed audiences.
Interest in applying AI to solve real-world business problems in a client-facing environment.

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 may be required beyond your home office / primary working location. Frequency and destination vary by project needs.

U.S. Compensation Information

For all locations, the good-faith reasonable annualized full-time compensation for this role is commensurate with competitive geographic market rates. Additionally, in some locations compensation may vary based on several factors including, but not limited to relevant experience, education, licensure/certifications, training and skill level.

Compensation for this role in the United States includes a monthly base salary of $9,000 and Bain's best-in-class benefits package (details listed below).

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

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