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 [1] 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 [2] 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 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
We are hiring an AI Engineer to 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
- 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.
English is mandatory. Spanish/Portuguese is a 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.
References
Visible links
1. https://www.bain.com/about/awards-recognition/
2. https://www.bain.com/careers/work-with-us/integrated-teams/ai-insights-solutions-ais/
