AI Engineer

Bain & Co. is seeking an AI Engineer for the Coro team to build next-generation AI-infused software and data products. The work focuses on LLM-driven features and agentic workflows, spanning rapid POCs, MVPs, and scaled enterprise deployments.

This role combines GenAI engineering with retrieval and knowledge pipeline development, end-to-end ML/DS delivery, and production-grade implementation for commercial environments. You will help turn ambiguous client needs into practical technical requirements, delivery tradeoffs, and plans.

What you will do

• Design and build GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
• Implement agentic workflows when they add clear value, including tool use, multi-step execution, and 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, including hybrid search, vector stores, graph databases, knowledge graphs, and traditional data platforms.
• Own key retrieval concerns such as indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
• Build robust agent capabilities across context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
• Integrate AI solutions into enterprise environments and workflows through APIs and data systems while balancing quality, latency, cost, privacy, and adoption.
• Translate unclear client goals into technical requirements, including tradeoffs and delivery plans.
• Build ML solutions end-to-end, covering data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
• Select appropriate methods across classical ML and deep learning, including sequence, text, and image models when relevant.
• Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and clear documentation.
• Demonstrate deep learning fluency, including transformer fundamentals and LLM pre-training versus post-training concepts such as instruction tuning and preference optimization approaches.
• Write clean, testable, maintainable code and ship AI services across the full SDLC: build, test, deploy, monitor, and iterate.
• Apply MLOps and GenAIOps practices including CI/CD, reproducibility, environment parity, and model/prompt/agent versioning with operational readiness.
• Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization.
• Support secure enterprise deployment with access controls, auditability, and responsible AI guardrails for sensitive and PII data.
• Create reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that can scale across client contexts.
• Communicate clearly with technical and non-technical stakeholders through working sessions, recommendations, and crisp technical documentation.
• Partner with Bain consultants to prioritize critical technical decisions that unlock business value.
• Support proposal shaping and scoping, including effort sizing, architecture options, risk assessment, and delivery roadmaps.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 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 (REST/gRPC) and integrating with enterprise systems.
• Hands-on experience building LLM-powered applications with delivery considerations including latency, cost, reliability, and security.
• Experience building advanced retrieval/search systems such as hybrid retrieval, vector search, and reranking, comfortable working across multiple data stores (vector, graph, relational/document/search).
• Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) using modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with strong judgment about when agentic approaches are appropriate.
• Experience creating reusable skills/tools/services for agent use, including MCP, with schema validation (e.g., Pydantic) to enforce reliable data contracts.
• Strong engineering practices including testing, code review, version control, and CI/CD, plus performance profiling.
• Experience deploying and operating services on AWS, GCP, and/or Azure with environment management, reliability, observability, and scaling.
• Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production.
• Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization, access controls, and PII/sensitive data handling.
• Experience training, validating, and testing ML models, including understanding of overfitting, generalization, and evaluation methodology.
• Practical experience with feature engineering and data preprocessing for real-world datasets.
• Familiarity with classical ML and deep learning and the ability to choose methods that match business and data constraints.
• Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking and model registry.
• Proven ability to operate in ambiguity, manage priorities, and deliver 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 and comfort working directly with clients.

Tools and technologies

• Python, REST, gRPC, LLM
• LangGraph, OpenAI Agents SDK, Pydantic AI, Pydantic, MCP
• AWS, GCP, Azure
• Docker, Kubernetes
• PyTorch, TensorFlow
• CI/CD, vector stores, graph databases/knowledge graphs, hybrid search, vector search, reranking
• APIs, data pipelines
• MLOps, GenAIOps, SDLC

Benefits

• Bain pays 100% individual employee premiums for medical, dental, and vision programs.
• 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.

Preferred

• MBA or PhD in a technical field.
• Background in consulting, professional services, or B2B analytics environments.
• Experience working with major AI ecosystem partners on real client deployments.

Compensation and location

• Location: Dallas, TX 75202 (onsite)
• Base salary: USD 128,500 - 171,500 per year
• U.S. compensation details: Includes base salary, annual discretionary performance bonus, and a 401(k) plan with an annual employer contribution based on years of service and Bain’s best-in-class benefits package.
• 401(k) contribution: 4.5% company contribution, increases after 3 years of service, and is 100% vested upon start date.