AI Engineer

Responsibilities

• Design and develop GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
• Implement agentic workflows where they create clear value, including tool use, multi-step execution, and human-in-the-loop controls, with emphasis on reliability, safety, and clear failure modes.
• Design and build advanced retrieval, search, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, and graph databases or knowledge graphs. Cover indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
• Build robust agent capabilities including context engineering, short-term and long-term memory and state management, orchestration, routing, and tool integration patterns.
• Integrate solutions into enterprise environments and workflows through APIs, data systems, and collaboration tools, managing tradeoffs across quality, latency, cost, privacy, and adoption.
• Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
• Build ML solutions end-to-end, including data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
• Choose appropriate methods across 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.
• Ship AI services across the full SDLC: build, test, deploy, monitor, and iterate.
• Apply MLOps and GenAIOps practices including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents.
• Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization.
• Design for secure enterprise deployment with access controls, auditability, appropriate data handling for sensitive and PII data, and responsible AI guardrails.
• Create reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that scale across client contexts.
• Communicate with technical and non-technical stakeholders by leading working sessions, presenting recommendations, and writing crisp technical documentation.
• Partner with Bain consultants to prioritize critical technical decisions that unlock business value.
• Support proposal shaping and scoping including 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), plus strong backend engineering 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 delivery considerations including latency, cost, reliability, and security.
• Experience building advanced retrieval/search systems (hybrid retrieval, vector search, reranking) and comfort working across multiple data stores including vector, graph, relational/document/search.
• Experience implementing agentic patterns for context management, tool integration, orchestration, and memory/state handling, using modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with judgment on when agentic approaches are and are not appropriate.
• Experience creating reusable skills, tools, and services for agent use, including MCP, with schema validation (Pydantic) for reliable data contracts.
• Strong engineering practices including testing, code review, version control, CI/CD, and performance profiling.
• Experience deploying and operating services on AWS, GCP, and / or Azure with 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 for AI systems including authentication/authorization, access controls, and PII/sensitive data handling.
• Experience training, validating, and testing ML models, including 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 ability to choose methods aligned to business and data constraints.
• Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking and model registry.
• Ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team.
• Excellent communication skills for explaining technical decisions and tradeoffs to mixed audiences.
• Strong stakeholder management skills and comfort working directly with clients.

Technologies

• Python, REST, gRPC
• LLM, LangGraph, OpenAI Agents SDK, Pydantic AI, Pydantic, MCP
• AWS, GCP, Azure
• Docker, Kubernetes
• PyTorch, TensorFlow
• CI / CD, vector stores, graph databases, knowledge graphs

Benefits

• Medical, dental and vision programs (Bain pays 100% of individual employee premiums).
• 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.
• 401(k) company contribution: 4.5% company contribution that increases after 3 years of service and is 100% vested upon start date.
• Annual discretionary performance bonus.

About Coro and the Commercial Excellence Practice

• Bain’s B2B Commercial Excellence practice develops a clear go-to-market strategy by building market understanding, designing sales coverage and capacity models, and creating an industrialized sales execution capability.
• Coro’s software and data solutions support this approach.
• Coro℠ is a business unit bringing together Bain’s proprietary SaaS and DaaS tools, including cloud-based software, online capability assessments, and advanced analytics focused on enabling Commercial Excellence for B2B companies.

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.