Expert Consultant, Coro, AI Engineer

About the role

Bain is a global consulting firm that is consistently recognized as one of the best places to work. We are currently the top-ranked consulting firm on Glassdoor's Best Place to Work list and have been named the #1 overall spot a record seven times.

Responsibilities

• Build AI-powered tools and products that drive real business outcomes
• Design and develop GenAI applications (e.g., copilots, workflow automation, decision support for commercial teams) 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)
• Create robust agent capabilities including context engineering, memory and 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 and testing, and performance analysis
• Create reproducible training and evaluation pipelines (versioning, experiment tracking, robust validation, clear documentation)
• Engineer for real delivery
• Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and 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 and policy optimization
• Build secure enterprise deployment: access controls, auditability, data handling for sensitive and 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

Requirements

• Core engineering and AI application skills
• 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 (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), with modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, and strong judgment about when agentic approaches are, and are not, appropriate
• Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (e.g., Pydantic) to enforce reliable data contracts
• 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
• 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

Qualifications

• 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

Skills

• Core engineering and AI application skills
• 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), with modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, and strong judgment about when agentic approaches are, and are not, appropriate
• Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (e.g., Pydantic) to enforce reliable data contracts
• 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
• 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

Benefits

Bain offers a comprehensive benefits and wellness program designed to help employees achieve personal independence, protection, and stability in the areas most important to them and their families. Some local governments in the United States require a good-faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

• In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range for this role is between $128,500-$171,500; placement within this range 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

Pay

The estimated annualized compensation for this role is as follows:

• In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range for this role is between $128,500-$171,500; placement within this range 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

Schedule

Not specified