Expert Consultant, Coro, AI Engineer

Please submit your resume/CV in English to be considered for this role.

Who you’ll work with

About the Commercial Excellence practice

Bain’s B2B Commercial Excellence practice works with clients to develop a clear go-to-market (GTM) strategy: building an in-depth understanding of market opportunity, designing effective sales coverage and capacity models to attack that opportunity, and creating an industrialized sales execution capability to ensure commercial success. Coro’s software and data solutions are an innovative and increasingly critical part of this approach.

Where you’ll fit within the team

About Coro℠

The Coro℠ business unit brings together Bain’s proprietary suite of software-as-a-service (SaaS) and data-as-a-service (DaaS) tools, including cloud-based software, online capability assessments, and advanced analytics, focused on enabling Commercial Excellence for B2B companies.

The Impact You'll Have

Bain’s Commercial Excellence work helps B2B companies turn market opportunity into booked revenue, through sharper go-to-market strategy, better sales coverage and capacity, and industrialized execution.

We are hiring an AI Engineer to join the Coro team and help build the next generation of Coro’s products: cutting-edge software and data tools infused with AI and agentic capabilities. Working across rapid proofs of concept (POCs), MVPs, and, where appropriate, scaled production deployments, you will design and implement LLM-driven features and agentic workflows that use tools, data, and enterprise systems to execute multi-step tasks reliably and safely, turning raw, heterogeneous client data into the structured analytics and synthesized outputs that case teams rely on in live engagements.

These capabilities power Coro’s product suite, such as MoneyMap™, which builds a granular, bottoms-up view of market opportunity, and the repeatable analytics workflows behind Bain’s B2B go-to-market work, across challenges such as opportunity mapping, indirect channel growth, and profitability analysis.

While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed, including building evaluation approaches, working with data pipelines, and developing or integrating ML models when they materially improve performance or reliability.

You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in commercial settings. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time, in keeping with Bain’s apprenticeship model.

What you’ll do

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), covering indexing strategies, metadata design, relevance tuning / reranking, freshness, caching, access controls, and source attribution.
• Build 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.
• 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).
• Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training concepts (e.g., instruction tuning and preference optimization approaches).

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.
• 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 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.

About you

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.
• 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 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.

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.