Expert Consultant, Coro, AI Engineer

Responsibilities

• Develop AI powered tools and products that drive meaningful business outcomes
• Design and implement GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
• Create agentic workflows with 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 structures and stores (hybrid search, vector stores, graph databases/knowledge graphs, and traditional data platforms) covering indexing, metadata design, 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 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
• Deliver end-to-end ML solutions: data preparation, feature engineering, model selection, training, validation and testing, and performance analysis
• Apply methods spanning 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 fluency with modern deep learning concepts, transformer fundamentals, and LLM pre-training versus post-training approaches (e.g., instruction tuning and preference optimization)
• Engineered for real delivery, writing clean, testable, maintainable code and shipping 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, instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization
• Design for 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 concise technical documentation
• Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value
• Support proposal shaping and scoping: 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 Python proficiency 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/search systems (hybrid retrieval, vector search, reranking) and comfort across data stores (vector, graph, relational/document/search)
• Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) with modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, and strong judgment about when agentic approaches are appropriate
• Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (Pydantic) to enforce reliable 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 (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)
• 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 select methods that match business and data constraints
• Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
• 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

Technologies

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

Benefits

• Bain pays 100 percent of employee premiums for medical, dental, and vision coverage
• Generous paid time off including parental leave, sick leave, and holidays
• Fully vested 401(k) company contribution
• Paid life and long-term disability insurance
• Annual fitness reimbursements
• 4.5 percent 401(k) company contribution, which increases after 3 years of service and is 100 percent vested on start date

Compensation and Location

• Base salary range 128,500 to 171,500 USD per year for certain locations (MA, NY, DC, GA, IL, TX, WA, CA); compensation elsewhere is commensurate with geographic market rates
• Annual discretionary performance bonus
• 401(k) plan with employer contributions based on years of service
• Comprehensive benefits and wellness program

Preferred Qualifications

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