Overview

What Makes Us a Great Place to Work

Responsibilities

• Design and develop GenAI applications (e.g., copilots, workflow automation, decision support) 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/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 ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/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 vs post-training concepts (e.g., instruction tuning and preference optimization approaches).
• Write clean, testable, maintainable code and ship AI services through the full SDLC: build test deploy monitor 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/policy optimization.
• Design for secure enterprise deployment: access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.
• Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.
• 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.

Qualifications

• Core engineering + AI application skills
• 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).
• Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.
• Cloud, platform, and production delivery experience
• 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
• 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.
• Working Model & Travel
• This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.
• Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.