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Today

AI Engineer

Bain & Co. Dallas, TX

Dallas, TX

Today

$129k - $172k/yr Full time Backend Developer Agentic Ai API

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Job Description

Bain & Co. is seeking an AI Engineer for the Coro team to build next-generation AI-infused software and data products. The work focuses on LLM-driven features and agentic workflows, spanning rapid POCs, MVPs, and scaled enterprise deployments.

This role combines GenAI engineering with retrieval and knowledge pipeline development, end-to-end ML/DS delivery, and production-grade implementation for commercial environments. You will help turn ambiguous client needs into practical technical requirements, delivery tradeoffs, and plans.

What you will do

Design and build GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
Implement agentic workflows when 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, graph databases, knowledge graphs, and traditional data platforms.
Own key retrieval concerns such as indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
Build robust agent capabilities across context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
Integrate AI solutions into enterprise environments and workflows through APIs and data systems while balancing quality, latency, cost, privacy, and adoption.
Translate unclear client goals into technical requirements, including tradeoffs and delivery plans.
Build ML solutions end-to-end, covering data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Select appropriate 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 deep learning fluency, including transformer fundamentals and LLM pre-training versus post-training concepts such as instruction tuning and preference optimization approaches.
Write clean, testable, maintainable code and ship AI services across the full SDLC: build, test, deploy, monitor, and iterate.
Apply MLOps and GenAIOps practices including CI/CD, reproducibility, environment parity, and model/prompt/agent versioning with operational readiness.
Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization.
Support secure enterprise deployment with access controls, auditability, and responsible AI guardrails for sensitive and PII data.
Create reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that can scale across client contexts.
Communicate clearly with technical and non-technical stakeholders through working sessions, recommendations, and crisp technical documentation.
Partner with Bain consultants to prioritize critical 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 such as hybrid retrieval, vector search, and reranking, comfortable working across multiple data stores (vector, graph, relational/document/search).
Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) using modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with strong judgment about when agentic approaches are appropriate.
Experience creating reusable skills/tools/services for agent use, including MCP, with schema validation (e.g., Pydantic) to enforce reliable data contracts.
Strong engineering practices including testing, code review, version control, and CI/CD, plus performance profiling.
Experience deploying and operating services on AWS, GCP, and/or Azure with environment management, reliability, observability, and scaling.
Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production.
Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization, access controls, and PII/sensitive data handling.
Experience training, validating, and testing ML models, including understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with classical ML and deep learning and the ability to choose methods that match business and data constraints.
Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking and model registry.
Proven ability to operate in ambiguity, manage priorities, and deliver 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 and comfort working directly with clients.

Tools and 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.

Compensation and location

Location: Dallas, TX 75202 (onsite)
Base salary: USD 128,500 - 171,500 per year
U.S. compensation details: 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 best-in-class benefits package.
401(k) contribution: 4.5% company contribution, increases after 3 years of service, and is 100% vested upon start date.

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