Site AI Engineer

This position can be consulting or contract-to-hire or full-time . Candidates must be in the local geographic area as the position fully onsite. Responsibilities Opportunity hunting and workflow redesig n – Lead Lean/Six Sigma discovery workshops; map value streams, assess process and data maturity, and log low-effort/high-impact AI use cases Process and data maturity assessmen t – Evaluate each job site's current workflows and underlying data; surface gaps that block AI adoption and develop phased improvement plans with Operations Excellence to establish the right process baseline before deploying agents Assess the market solution s – Evaluate off-the-shelf and platform tools; launch pilots, measure impact, and scale wins Rapid AI-agent build s – Convert user stories into production-ready agents in Copilot Studio/Power Apps/Automate, ChatGPT Enterprise, or code-first frameworks within days; wire them to Teams/SharePoint on the front end and Databricks Lakehouse or other sources on the back end Enterprise-grade engineering & LLMOp s – Build RAG pipelines backed by Delta tables, Unity Catalog, and Databricks Vector Search; automate infra with GitHub Actions / Posit; monitor latency, cost, adoption, and drift .Data integration s – Partner with Data Engineering to design and maintain ETL pipelines, API integrations, and event-driven connectors feeding RAG and agents Cross-cloud orchestratio n – Blend OpenAI, Azure OpenAI, and AWS Bedrock behind secure custom connectors; package agents for seamless rollout Change enablemen t – Train crews, gather feedback, iterate, and track adoption and ROI metrics; apply influence model principles to embed agents into daily routines and SOPs, and track behavior change KPIs Stakeholder communicatio n – Brief project leadership and clients on agent impact in clear business terms; contribute use cases and playbooks for "Construction Site of the Future. "Escalation & hand-of f – Draft clear user stories, data specs, and acceptance criteria for any complex solution that requires the central AI Solution Engineers or Data Engineering / Data Science team to lean in Qualification: 4+ years in AI engineering/full-stack data applications or data science, including 2+ years building a production LLM/RAG solution Bachelor's in CS, Engineering, Physics, or a related field; Master's preferred Prior hands-on work in construction or heavy process industries (manufacturing, oil & gas, chemicals) is a significant plus Demonstrated process excellence background (Lean/Six Sigma Green Belt or equivalent) with experience diagnosing process and data gaps and supporting change management plans with Operations Excellence Strong facilitation and communication skills Hands-on expertise with Copilot Studio, Power Apps/Automate, custom connectors, and CoE Toolkit governance Programming & data stack: Python, SQL, Databricks Lakehouse, vector store DevOps & IaC: GitHub Actions (or Azure DevOps) and Posit Workbench/Connect automation or comparable CI/CD tooling; strong Git/GitHub workflow discipline Integration & ETL skills: Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipeline Willing and able to travel and work on an active jobsite