Join Bain Coro as an Expert Consultant, AI Engineer, delivering GenAI powered tools and agentic AI workflows for B2B Commercial Excellence. This onsite role in Seattle spans projects from proof-of-concept to production deployments, translating AI innovation into measurable business outcomes. The position offers a competitive annual salary of USD 128,500 to 171,500, and requires 3–5+ years of AI/ML engineering experience along with a Bachelor’s degree in Computer Science or Engineering, or equivalent practical experience.
Benefits
- Health insurance (medical, dental, and vision) with Bain paying 100% of individual premiums
- Generous paid time off, including parental leave, sick leave, and paid holidays
- Fully vested 401(k) company contribution
- 4.5% 401(k) company contribution (vesting after 3 years)
- Life and Long-Term Disability insurance
- Annual fitness reimbursements
- Annual discretionary performance bonus
Responsibilities
- Develop AI-enabled tools and products that deliver measurable business outcomes
- Design and implement GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
- Implement agentic workflows with emphasis on reliability, safety, and clear failure modes, including tool use, multi-step execution, and human-in-the-loop controls
- Architect and build advanced search, retrieval, and knowledge pipelines across diverse data stores (hybrid search, vector stores, graph databases / knowledge graphs, and traditional data platforms), addressing indexing, metadata, 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 end-to-end: data preparation, feature engineering, model selection, training, validation and testing, and performance analysis
- Apply 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
- Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training approaches
- 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, 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 that scale across client contexts
- Thrive in a client-facing consulting environment: communicate clearly with technical and non-technical stakeholders, lead sessions, present recommendations, and document technical details
- Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value and support proposal shaping and scoping
Requirements
- 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 (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 working across vector, graph, relational, document, and search data stores
- 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
- Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (Pydantic) to enforce 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, with focus on reliability and observability
- Experience with Docker and Kubernetes and operating services in production
- Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization and PII handling
- Experience training, validating, and testing ML models; solid 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 (classic and deep learning) and the ability to select appropriate methods
- Familiarity with deep learning frameworks (PyTorch / TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
- Ability to operate effectively in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team
- Excellent interpersonal and communication skills, with the ability to explain complex technical decisions to mixed audiences
- Strong stakeholder management and client-facing capabilities
- MBA or PhD in a technical field (preferred)
- Background in consulting, professional services, or B2B analytics environments (preferred)
- Experience collaborating with major AI ecosystem partners on real client deployments (preferred)
Technologies
- Python
- REST
- gRPC
- LangGraph
- OpenAI Agents SDK
- Pydantic AI
- PyTorch
- TensorFlow
- Docker
- Kubernetes
- AWS
- GCP
- Azure
- Vector stores
- Graph databases
- Knowledge graphs