Expert Consultant, Coro, AI Engineer

About the role

Bain is a leading 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 awarded 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)
• 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
• 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
• 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)
• 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
• Experience building APIs / services (e.g., REST / gRPC)
• Hands-on experience building LLM-powered applications
• Experience with modern deep learning concepts
• Experience with deep learning frameworks (e.g., PyTorch / TensorFlow)
• Experience with ML lifecycle tooling (e.g., experiment tracking, model registry, feature store concepts)
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning frameworks
• Experience with ML lifecycle tooling
• Experience with data science and machine learning
• Experience with classical ML and deep learning
• Experience with feature engineering and data preprocessing
• Experience with ML algorithms
• Experience with deep learning