AI Engineer

Role Summary

As the technical engineer driving the company's enterprise AI programs, you will design, build, and support AI solutions that scale across corporate functions and construction sites nationwide. You will work closely with executives, functional leaders, and onsite AI Engineers to identify high-impact opportunities, translate business needs into technical solutions, and ensure consistency in deployment. This role is both hands-on in engineering and strategic in scope, advancing the company's "Construction Site of the Future” vision through corporate-driven AI initiatives.

• Enterprise Workflow Analysis: Collaborate with corporate functions (Finance, HR, Operations, Safety) and project leaders to identify pain points and AI opportunities that can be standardized across the company.
• AI Agent Development: Build and deploy production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or Python-based frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
• RAG Pipelines & LLMOps: Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift across sites.
• Cross-Cloud Engineering: Implement and maintain integrations across OpenAI, Azure OpenAI, and AWS Bedrock services with secure custom connectors.
• Data Integration: Partner with Data Engineering to deliver ETL/ELT pipelines, APIs, and event-driven connectors that enable enterprise-wide AI solutions.
• Adoption & Change Enablement: Support onboarding and training for both corporate users and field teams, track adoption metrics, and iterate solutions for stronger business impact.
• Documentation & Communication: Produce clear technical documentation, user stories, and specs for AI solutions, while translating outcomes into business value for corporate leadership.
• Governance & Compliance: Ensure all AI solutions meet the company's data governance, security, and compliance requirements.

• 4+ years in AI engineering, data science, or ML-focused software engineering.
• Proven experience building and deploying multiple AI agents in production environments.
• 2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
• Strong proficiency in Python, SQL, and Databricks (Spark/Hadoop equivalents acceptable).

• Hands-on experience with Copilot Studio, Power Apps/Automate, API development, and integration.
• Familiarity with CI/CD workflows (Git Hub Actions, Azure Dev Ops) and workflow automation.
• Solid understanding of ETL/ELT, REST/Graph
  
  QL APIs, and enterprise data engineering practices.
• Experience working in construction, engineering, or other process-heavy industries.
• Advanced technical degree or certifications in AI/ML engineering.