Expert Consultant, Coro, AI Engineer

AI Engineer on Bain's Coro team focused on GenAI powered tools and data analytics for Commercial Excellence, emphasizing LLM driven features, agentic workflows, and production-ready AI applications.

Responsibilities

• Develop AI powered tools and products that deliver measurable business impact
• Design and implement GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
• Implement agentic workflows with clear value, including tool use, multi-step execution, and human-in-the-loop controls, prioritizing reliability, safety, and clear failure modes
• Architect and build advanced search, retrieval, and knowledge pipelines across diverse data stores (hybrid search, vector stores, graph databases/knowledge graphs, and traditional data platforms), addressing indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution
• Develop robust agent capabilities including context engineering, memory and state management, orchestration, routing, and tool integration patterns
• Integrate AI solutions into enterprise workflows and environments via APIs and data systems, balancing quality, latency, cost, privacy, and adoption
• Translate ambiguous client needs into concrete technical requirements, tradeoffs, and delivery roadmaps
• Deliver end-to-end ML solutions, from data preparation and feature engineering to model selection, training, validation, testing, and performance analysis
• Select appropriate ML methods across classical and deep learning, including sequence, text, and image models when relevant
• Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation
• Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training approaches
• Engineer for production delivery with clean, testable, maintainable code and full SDLC shipping of AI services
• Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents
• Establish evaluation and observability for GenAI and agentic systems with tracing, instrumentation, regression tests, automated scoring, and prompt/policy iteration
• Design secure enterprise deployments with access controls, auditability, sensitive data handling, and guardrails for responsible AI
• Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts
• Thrive in a client facing consulting environment, communicating clearly with technical and non-technical stakeholders
• Lead working sessions, present recommendations, and document technical considerations crisply
• Collaborate with Bain consultants to prioritize crucial technical decisions that unlock value
• Support proposal shaping and scoping with 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 with strong backend 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 attention to latency, cost, reliability, and security
• Experience building advanced retrieval and search systems (hybrid retrieval, vector search, reranking), across vector, graph, relational, document, and search data stores
• Experience implementing agentic patterns with context management, tool integration, orchestration, and memory/state handling, using frameworks like LangGraph, OpenAI Agents SDK, Pydantic AI or custom agent loops
• Experience creating reusable skills, tools, and services for agent use, with schema validation (Pydantic) to enforce data contracts
• Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling
• Experience deploying and operating services on AWS, GCP, and/or Azure, including 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, PII handling, and enterprise risk controls
• 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, from classical to deep learning, and ability to match methods to business and data constraints
• Familiarity with deep learning frameworks (PyTorch, TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
• Ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team
• Excellent interpersonal and communication skills; able to explain technical decisions to mixed audiences
• Strong stakeholder management and comfort working directly with clients

Technologies

• Python
• REST, gRPC
• LangGraph
• OpenAI Agents SDK
• Pydantic AI
• PyTorch, TensorFlow
• Docker, Kubernetes
• AWS, GCP, Azure
• MCP
• Graph databases

Compensation and Benefits

Location: Boston, MA onsite

Salary range: USD 128,500 - 171,500 per year

• Annual discretionary performance bonus
• 4.5% 401(k) company contribution, increases after 3 years of service, fully vested on start date
• 100% paid employee premiums for medical, dental, and vision insurance
• Generous paid time off including parental leave, sick leave, and holidays
• Paid Life and Long-Term Disability insurance
• Annual fitness reimbursements

What makes us a great place to work

Bain is consistently recognized as a top global employer, with a culture that emphasizes diverse perspectives and professional growth. We focus on building extraordinary teams in an inclusive environment where individuals can thrive professionally and personally.

U S Compensation Information

The stated compensation reflects baseline ranges and may vary by location. In Massachusetts and select states, the range is 128,500 to 171,500 annually, with additional discretionary compensation potential. Benefits include comprehensive health coverage, a robust 401(k) plan with employer contributions, and other workplace advantages.