On Bain & Co. and the Coro AI Engineer team, this Expert Consultant role centers on delivering GenAI powered tools and data-driven capabilities for B2B Commercial Excellence. The position spans the full lifecycle from concept to production, in a client-facing consulting context, with emphasis on agentic workflows, retrieval pipelines, and ML models.
Responsibilities
- Design and implement GenAI applications such as copilots, workflow automation, and decision-support aids for commercial teams using modern large language model stacks.
- Develop agentic workflows that add measurable value, including tool usage, multi-step execution, and human-in-the-loop controls, with a focus on reliability, safety, and clear failure modes.
- Architect and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores (hybrid search, vector repositories, knowledge graphs, and traditional data platforms), covering indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution.
- Create robust agent capabilities around context engineering, short- and long-term memory, orchestration, routing, and tool integration patterns.
- Integrate AI solutions into enterprise environments and workflows (APIs, data systems, collaboration tools), balancing quality, latency, cost, privacy, and adoption considerations.
- Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
- Deliver end-to-end ML solutions including data preparation, feature engineering, model selection, training, validation, testing, and performance analysis.
- Apply appropriate methods spanning classical ML and deep learning, including sequence, text, and image models when relevant.
- Develop reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation.
- Demonstrate fluency with modern deep learning concepts, transformer fundamentals, and LLM pre-training versus instruction tuning and preference optimization.
- Produce clean, testable, maintainable code and deliver AI services through the full SDLC: build, test, deploy, monitor, and iterate.
- Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model and prompt versioning, and operational readiness.
- Establish evaluation and observability for GenAI and agentic systems: tracing, instrumentation, automated scoring where appropriate, and iterative prompt and policy optimization.
- Design for secure enterprise deployment: access controls, auditing, sensitive data handling, and responsible AI guardrails.
- Build reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that scale across client contexts.
- Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and document technical decisions concisely.
- Collaborate with Bain consultants to prioritize 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, with solid 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 designing advanced retrieval / search systems (hybrid retrieval, vector search, reranking) and working across multiple data stores (vector, graph, relational, document, and 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, plus good judgment on when to apply agentic approaches.
- Experience creating reusable skills, tools, and services 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, or Azure, including environment management, reliability, observability, and scaling.
- Experience with Docker and Kubernetes, 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 handling, enterprise risk controls).
- Experience training, validating, and testing ML models; solid understanding of overfitting, generalization, and evaluation methodologies.
- Practical experience with feature engineering and data preprocessing for real-world datasets.
- Familiarity with a broad set of ML algorithms (classical and deep learning) and the ability to select methods aligned with 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 effectively in ambiguity and complexity, manage priorities, and deliver outcomes independently or within a team.
- Excellent interpersonal and communication skills, with the ability to explain technical decisions, tradeoffs, and results to mixed audiences.
- Strong stakeholder management skills and comfort working directly with clients.
Technologies
- Python
- REST
- gRPC
- LangGraph
- OpenAI Agents SDK
- Pydantic AI
- Docker
- Kubernetes
- AWS
- GCP
- Azure
- PyTorch
- TensorFlow
- MCP
Benefits
- Bain pays 100% of individual employee premiums for medical, dental, and vision programs.
- 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.
- 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date.
Compensation and Location
Location: Washington, DC (onsite).
Salary: USD 128,500 - 171,500 per year.
Compensation details: In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California the good-faith annualized salary range is 128,500 to 171,500; placement within this range depends on factors including experience, education, certifications, training, and skill level. For all other locations, compensation is aligned with local market rates.
Additional compensation may include an annual discretionary performance bonus. The role includes a 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date.
Please submit your resume/CV in English to be considered for this role.
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