AI Engineer

Bain & Co. is hiring an AI Engineer for the Coro team to build AI-infused software and data products. This onsite role in Dallas, TX 75202 focuses on delivering LLM-driven features and agentic workflows through rapid proof-of-concepts, MVPs, and scaled enterprise deployments.

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 add clear value, including tool use, multi-step execution, and 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, including hybrid search, vector stores, and graph databases or knowledge graphs, and traditional data platforms.
• Cover end-to-end retrieval pipeline needs, including indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
• Build robust agent capabilities including context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
• Integrate solutions into enterprise environments and workflows via APIs, data systems, and collaboration tools while balancing quality, latency, cost, privacy, and adoption.
• Convert 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.
• Apply appropriate ML methods across classical ML and deep learning, including sequence, text, and image models when relevant.
• Create reproducible training and evaluation pipelines using versioning, experiment tracking, robust validation, and clear documentation.
• Demonstrate fluency with deep learning fundamentals and LLM training concepts, including transformer fundamentals and pre-training versus post-training approaches such as instruction tuning and preference optimization.
• Write clean, testable, maintainable code and ship AI services across the full SDLC, including build, test, deploy, monitor, and iterate.
• Apply MLOps and GenAIOps practices, including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents with operational readiness.
• Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring when appropriate, and iteration loops for prompt and policy optimization.
• Design for secure enterprise deployment using access controls, auditability, secure data handling for sensitive data and PII, 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 by leading working sessions, presenting recommendations, and writing crisp technical documentation.
• Work 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 (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 delivery considerations including latency, cost, reliability, and security.
• Experience building advanced retrieval/search systems, including hybrid retrieval, vector search, and reranking, and comfort working across multiple data stores such as vector, graph, relational/document/search.
• Experience implementing agentic patterns including context management, tool integration, orchestration, and memory/state handling using modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with strong judgment on where agentic approaches are appropriate.
• Experience creating reusable skills, tools, and services for agent use, including MCP, with schema validation (Pydantic) to enforce 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, including environment management, reliability, observability, and scaling.
• Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production, including debugging, performance, and resilience.
• Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization, access controls, PII/sensitive data handling, and enterprise risk controls.
• Experience training, validating, and testing ML models with strong 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 across classical ML and deep learning and ability to select methods that match business and data constraints.
• Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking, model registry, and feature store concepts.
• Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or collaboratively.
• Excellent interpersonal and communication skills, including explaining technical decisions, tradeoffs, and results 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
• Hybrid search, vector search, reranking
• APIs
• Data pipelines
• MLOps, GenAIOps
• SDLC

Benefits

• Bain pays 100% 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.

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.

U.S. Compensation Information

• Compensation includes base salary, annual discretionary performance bonus, and a 401(k) plan with an annual employer contribution based on years of service and Bain’s benefits package.
• In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range for this role is $128,500-$171,500.
• Placement within the range varies based on factors including experience, education, licensure/certifications, training, and skill level.
• Annual discretionary performance bonus and additional elements of discretionary compensation may apply.
• 401(k) company contribution: 4.5%, increasing after 3 years of service and 100% vested upon start date.
• For all other locations, the good-faith, reasonable annualized full-time salary range is commensurate with competitive geographic market rates and varies based on factors including experience, education, licensure/certifications, training, and skill level.
• In Massachusetts, it is unlawful to require or administer a lie detector test as a condition of employment or continued employment; violations may result in criminal penalties and civil liability.