AI Embedded Business Automation Engineer

Hines•Houston, TX

22h

About The Position

The AI Embedded Business Automation Engineer works with functional departments to identify opportunities where AI, automation, and improved system design can enhance business operations. The role partners directly with business teams to understand current workflows, uncover manual handoffs, rework, delays, and exception-heavy processes, and design, prototype, and deliver technical solutions that make daily work faster, smarter, and more scalable. The engineer independently translates business needs and strategic objectives into technical requirements, architecture, and scalable AI and data solutions. They also communicate technical concepts, decisions, limitations, risks, trade-offs, and expected outcomes as clear business-oriented recommendations for leaders and other stakeholders. The role owns initiatives from discovery through delivery, adoption, and impact measurement. It builds and maintains AI-enabled workflows and integrations, embeds security and responsible AI controls, supports production systems, enables business users, and documents reusable patterns aligned with IT governance and architecture standards.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science, Mathematics, or a related field; equivalent practical experience may be considered.
5–7+ years of experience in software engineering, automation engineering, cloud engineering, data engineering, integration development, or related technical roles, including delivery of AI or Generative AI solutions in a business environment.
Experience building workflow automations, APIs, integrations, copilots, assistants, or AI-enabled business applications using Microsoft, Azure, OpenAI, ChatGPT Enterprise, or comparable enterprise platforms.
Experience supporting production systems, partnering with cross-functional stakeholders, and translating business needs into secure, scalable technical solutions.
Strong product judgment, communication skills, and delivery orientation, including the ability to release useful increments quickly, recognize when a more durable solution is needed, and write clear specifications, documentation, and updates.
Demonstrated ability to deliver reliable code, services, integrations, or automations used in day-to-day business operations.
Ability to independently lead AI and data architecture decisions, establish and implement architectural standards, and own complex technical design and delivery with limited developmental support.
Proficiency in Python and ability to write clean, tested, maintainable code and contribute to shared production codebases.
Hands-on experience applying LLMs and Generative AI to business problems, including prompt engineering, LLM APIs, retrieval-augmented generation, AI-enabled workflows, prototypes, assistants, or agents.
Experience using AI-assisted development tools and building tool-using workflows or custom agents that leverage MCP or similar integration patterns and connect securely to enterprise systems, APIs, data sources, and business applications.
Ability to turn business problems into technical specifications, solution designs, implementation plans, and working software, and explain these clearly to nontechnical partners.
Working knowledge of several enterprise engineering areas, including cloud infrastructure, data engineering, SQL, pipelines, integration patterns, automation tooling, IAM, APIs, and enterprise application development.
Familiarity with information security fundamentals, including authentication, authorization, encryption, secrets management, safe data practices, least-privilege access, auditability, and responsible AI guardrails.
Strong business acumen and consultative skills, including the ability to assess business challenges, evaluate solution alternatives, and explain the strategic and financial implications of technical decisions.
Experience designing AI-enabled business process automations across functions such as finance, legal, operations, or other enterprise teams.

Nice To Haves

Experience with Microsoft and OpenAI tools such as Azure OpenAI Service, OpenAI APIs, ChatGPT Enterprise, Microsoft Copilot Studio, Azure AI Foundry, Semantic Kernel, OpenAI Agent SDK, Power Platform, or comparable platforms.
Familiarity with advanced agent, retrieval, and orchestration patterns, including vector search, iPaaS tools, LangGraph, LangChain, LlamaIndex, or similar frameworks.
Familiarity with TypeScript, JavaScript, Go, or Java.
Experience designing AI-enabled business process automations across finance, legal, operations, or other enterprise teams.

Responsibilities

Engage business leaders to understand strategic priorities, identify opportunities, define requirements, and translate needs into practical, scalable AI, data, and technology solutions.
Present architectural recommendations and strategic proposals to senior leadership, communicating technical concepts, business implications, risks, trade-offs, and expected outcomes to technical and nontechnical audiences.
Co-design AI-enabled process improvements with subject matter experts, translating operational challenges into practical workflows that improve efficiency, accuracy, user experience, and business capacity.
Translate business requirements into technical specifications, solution designs, implementation plans, and delivery milestones.
Build agentic and automation solutions end-to-end in partnership with business teams, teammates, and senior engineers.
Prototype, test, and iterate on AI-driven automations, assistants, agents, and workflows using Python, LLM APIs, RAG patterns, agent frameworks, workflow orchestration, and enterprise platforms.
Develop and maintain integrations with enterprise systems, APIs, and data sources, including SharePoint, Teams, Outlook, ERP platforms, Salesforce, and internal applications.
Design and implement AI solution components such as prompt orchestration, RAG pipelines, document ingestion, vector search, backend services, automation logic, data pipelines, models, and secure access patterns.
Support adoption through SME enablement, practical training, feedback loops, and usage-based refinement.
Embed security, privacy, compliance, and responsible AI controls throughout the solution lifecycle, including data classification, least-privilege access, secrets management, auditability, and safe data handling.
Deploy, monitor, troubleshoot, optimize, and continuously improve production AI and automation solutions, focusing on reliability, performance, security, governance, cost management, and real-world usage.
Work cross-functionally with engineering, product, security, legal, and business teams to move solutions from concept through pilot, production launch, adoption, support, and ongoing enhancement.
Document solution architecture, implementation decisions, support procedures, runbooks, and best practices, and create repeatable agent, automation, integration, and data patterns aligned with IT governance and architecture standards.
Define success metrics and measure solution impact, including cycle-time reduction, quality improvements, adoption, reliability, and unlocked business capacity.
Communicate project progress, risks, decisions, and results to technical and nontechnical audiences, tailoring detail to the audience.
Build alignment across business and technology teams, facilitate decisions, and set clear expectations for scope, timing, ownership, and adoption.
Own AI and automation initiatives from stakeholder discovery through launch, adoption, and measurement of business impact.