AI Embedded Business Automation Engineer

About the role

Join Hines, a global real estate investment, development, and property management firm, and work within functional departments across the organization to enhance business operations through AI, automation, and better system design.

Responsibilities

• Partner with business stakeholders and subject matter experts to understand current workflows, identify manual handoffs, rework, delays, and exception-heavy processes, 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.
• 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.
• Define success metrics and measure solution impact, including cycle-time reduction, quality improvements, adoption, reliability, and unlocked business capacity.

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.
• 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.
• Familiarity with information security fundamentals, including authentication, authorization, encryption, secrets management, safe data practices, least-privilege access, auditability, and responsible AI guardrails.