AI Embedded Business Automation Engineer

Hines•New York City, NY

About The Position

When you join Hines, you will embark on a career journey fueled by vision and guided by leaders who set the standards of our industry. Our legacy is rooted in innovation and excellence, earning us a spot on Fast Company’s esteemed annual list of the World’s Most Innovative Companies, as well as recognition as one of U.S. News & World Report’s Best Companies to Work For in 2024. Discover endless opportunities to grow and make your mark at Hines. We are looking for an AI Embedded Business Automation Engineer to work within functional departments across the organization to uncover opportunities where AI, automation, and better system design can meaningfully improve business operations. This role will work directly with the business teams to understand how work happens today, pinpoint manual handoffs, rework, delays, and exception-heavy processes, and then design, prototype, and deliver technical solutions that make day-to-day work faster, smarter, and more scalable. As part of IT, this role is expected to build within enterprise standards for infrastructure, data, integration, automation, information security, governance, and production support. The work will range from AI-enabled workflow redesign and secure data pipelines to intelligent agents, system integrations, and automations that are reliable enough to run in production and useful enough to become part of how teams operate.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science, Mathematics, or a related field; equivalent practical experience may also be considered.
3+ years of experience in software engineering, automation engineering, cloud engineering, data engineering, integration development, or related technical roles, including experience delivering 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, with the ability to release useful increments quickly, recognize when a more durable solution is needed, and write specs, docs, and updates that are clear without additional explanation.
Demonstrated ability to deliver reliable code, services, integrations, or automations used in day-to-day business operations.
Required coding proficiency in Python, with the ability to write clean, tested, maintainable code and contribute effectively to shared production codebases.
Hands-on experience applying LLMs and Generative AI to real 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 agent integration patterns and connect securely to enterprise systems, APIs, data sources, and business applications.
Demonstrated ability to turn business problems into technical specifications, solution designs, implementation plans, and working software that can be explained clearly to non-technical partners.
Working knowledge across several core 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.

Responsibilities

Partner directly with business stakeholders and subject matter experts to understand current workflows, identify manual handoffs, rework, delays, exception-heavy processes, and other improvement opportunities, and prioritize AI automation efforts based on value, feasibility, risk, and scalability.
Co-design AI-enabled process improvements with SMEs, translating operational challenges into practical workflows that improve efficiency, accuracy, user experience, and business capacity.
Translate business requirements into clear technical specifications, solution designs, implementation plans, and delivery milestones, then build agentic and automation solutions end-to-end in partnership with business teams, teammates, and senior engineers.
Rapidly prototype, test, and iterate on AI-driven automations, assistants, agents, and workflow solutions using Python, LLM APIs, RAG patterns, agent frameworks, workflow orchestration, and enterprise platforms.
Develop and maintain integrations with enterprise systems such as SharePoint, Teams, Outlook, ERP platforms, Salesforce, internal applications, APIs, and data sources to enable end-to-end intelligent workflows.
Design and implement trustworthy AI solution components, including 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 so business teams actively shape and sustain the workflows being introduced.
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.
Remain accountable for production quality by deploying, monitoring, troubleshooting, optimizing, and continuously improving AI and automation solutions with a focus 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 that scale across functions while aligning 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.