Site AI Engineer

Aegistech; Boston (MA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Aegistech is seeking an AI engineering leader for a long-term, hybrid role in the Boston area. The position supports consulting, contract-to-hire, or full-time engagement and involves designing and deploying production AI agents across Copilot Studio, Power Apps, and modern data stacks.

You will drive discovery, build scalable data workflows, and collaborate with Operations Excellence to optimize processes and measure ROI. Local candidates preferred.

Qualifications

• 4+ years in AI engineering/full-stack data applications or data science with 2+ years building production LLM/RAG solutions.
• Bachelor’s in CS, Engineering, Physics, or related field; Master’s preferred.
• Experience in construction or heavy-process industries is a significant plus.
• Lean/Six Sigma background and change-management collaboration are advantageous.

Responsibilities

• Lead discovery workshops to map value streams and AI use cases.
• Build production-ready agents using Copilot Studio/Power Apps/Automate.
• Evaluate market solutions, pilot deployments, and measure ROI.
• Develop and maintain data pipelines with ETL/ELT practices.
• Collaborate across data engineering and DevOps to ensure scalable implementation.

Skills

AI engineering

Full-stack development

LLM/RAG

Copilot Studio

Power Apps

Python

SQL

Databricks Lakehouse

GitHub Actions

CI/CD

Education

Bachelor's in CS/Engineering

Master's preferred

Tools

Databricks Lakehouse

Power Apps/Automate

Copilot Studio

GitHub Actions

Job description

This position can be consulting or contract-to-hire or full-time. Candidates must be in the local geographic area as the position is hybrid 4-days in the office. This is a LONG-TERM project! Great opportunity with terrific company!

Responsibilities

• 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 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 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 GitHub 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

Qualification

• 4+ years in AI engineering/full-stack data applications or data science, including 2+ years building 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
• Hands‑on expertise with Copilot Studio, Power Apps/Automate, custom connectors, and CoE Toolkit governance
• Programming & data stack: Python, SQL, Databricks Lakehouse, vector stores
• 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