AI Solutions Engineer

Hillwood IT is hiring an AI Solutions Engineer to build secure, reliable, production-grade AI solutions from approved business use cases. The role develops GenAI applications, automations, agents, integrations, and RAG-enabled workflows while establishing reusable technical patterns and supporting governance and production readiness.

Responsibilities:

• Partner with AI business solution leads to translate approved business requirements, success metrics, and pilot criteria into technical solution designs and solutions
• Design and build production-grade GenAI applications, automations, agents, and RAG-enabled workflows for approved business use cases
• Create technical designs suitable for Architecture Review Board (ARB), security, and production readiness review
• Develop reusable solution patterns, templates, and implementation standards that can be applied across multiple use cases and divisions
• Balance speed of delivery with reliability, maintainability, and enterprise supportability
• Build, iterate, and deliver production-grade GenAI solutions for approved Horizon 1 use cases
• Design and implement agentic workflows using orchestration frameworks and multi-step agents that perform real actions
• Integrate AI solutions with Hillwood’s existing technology stack, including Claude API, ChatGPT, Copilot, Azure Functions, Power Automate, JDE, Yardi, Salesforce, SharePoint, Medius, Databricks, and other enterprise platforms
• Develop APIs, Azure Functions, workflow automations, backend services, data connectors, and application components required to operationalize AI solutions
• Own technical solution quality, including prompt engineering, structured outputs, output validation, error handling, evaluation, logging, monitoring, and reliability for business-critical use
• Develop AI coding assistant support and CI/CD pipeline integrations for enterprise environments
• Operate within Hillwood’s risk-tier governance framework, ensuring ARB review and lifecycle gates for Medium and High-risk solutions
• Implement Retrieval-Augmented Generation (RAG) pipelines, document intelligence workflows, structured extraction workflows, and contextual intelligence patterns
• Build and maintain reusable components for document parsing, chunking, metadata enrichment, embeddings, retrieval, citations, validation, and source traceability
• Support the creation of technical standards for prompts, tools, agents, evaluators, test datasets, and deployment patterns
• Partner with data teams to connect AI workflows to enterprise data platforms, reporting layers, and business systems
• Support discovery, scoping, and prioritization by advising on technical feasibility, implementation complexity, dependencies, risks, and support needs
• Support Steering Committee, division leadership, and ARB presentations by preparing technical architecture, demo environments, implementation status, risk considerations, and production readiness inputs
• Partner with Division AI Champions and business stakeholders to ensure solutions are usable, explainable, and aligned to business workflows
• Prepare or contribute to technical documentation, support readiness assessments, adoption materials, and scale recommendations
• Communicate technical concepts clearly to non-technical stakeholders when needed, while partnering with AI business solution leads on executive framing and ROI narratives