Site AI Engineer Job Las Vegas area,Nevada USA,Software Development

Opportunity hunting and workflow redesign – 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 assessment – Evaluate each jobsite’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 solutions – Evaluate off-the-shelf and platform tools; launch pilots, measure impact, and scale wins.
Rapid AI-agent builds – 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 & LLMOps – Build RAG pipelines backed by Delta tables, Unity Catalog, and Databricks Vector Search; automate infra with Git Hub Actions / Posit; monitor latency, cost, adoption, and drift.
Data integrations – Partner with Data Engineering to design and maintain ETL pipelines, API integrations, and event-driven connectors feeding RAG and agents.
Cross-cloud orchestration – Blend OpenAI, Azure OpenAI, and AWS Bedrock behind secure custom connectors; package agents for seamless rollout.
Change enablement – 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 communication – 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-off – 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.

Qualifications

4+ years in AI engineering / full-stack data applications or data science, including 2+ years building production LLM/RAG solutions.
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 stores.
Dev Ops & IaC:
Git Hub Actions (or Azure Dev Ops) and Posit Workbench/Connect automation or comparable CI/CD tooling; strong Git/Git Hub workflow discipline.
Integration & ETL skills:
Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/Graph

QL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipelines.
Willing and able to travel and work on active job sites.

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