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Expert Consultant, Coro, AI Engineer

Bain & Co. Seattle, WA

Seattle, WA

Today

$129k - $172k/yr Full time Agentic Ai Ai Workflows Artificial Intelligence

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Job Description

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 (classical 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

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