Genaiops Engineer Jobs

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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 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.

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