What Suffolk Construction offers
Suffolk supports you with a comprehensive total rewards program designed to sustain you and your family physically, emotionally, and financially. Expect competitive pay and a robust benefits package that prioritizes wellness, financial security, and work-life balance.
- Competitive salaries
- Auto allowances and gas cards for certain roles
- Market-leading medical and mental health benefits
- Dental and vision insurance
- Virtual care options for physical therapy and primary care
- Generous paid time off
- 401(k) with employer match
- Company paid and voluntary life insurance
- Tax deferred savings accounts
- Backup daycare days
- Short- and long-term disability
- Commuter benefits
Location and role context
Location: Temple, Texas, on-site
As a Site AI Engineer on Suffolk’s construction projects, you act as an on-site catalyst who turns AI ideas into functioning solutions. You partner with AI Champions to redesign workflows, deploy AI agents, and coordinate with the central AI Studio to advance the Construction Site of the Future.
Role overview
You will work across project teams as the on-site AI catalyst, uncovering pain points, simplifying reporting, speeding RFIs, supporting lookahead planning, materials tracking, and more. When needed, you draft user stories and coordinate development with the central AI Studio to scale prototypes into enterprise-ready applications.
Responsibilities
- Identify opportunities and redesign workflows by leading discovery, mapping value streams, assessing process and data maturity, and logging high-impact, low-effort AI use cases
- Assess current jobsite workflows and underlying data; surface gaps blocking AI adoption and develop phased improvement plans with Operations Excellence to establish the right baseline before deploying agents
- Evaluate off-the-shelf and platform tools; run pilots, measure impact, and scale successful deployments
- Translate user stories into MVP AI agents using AWS Bedrock/SageMaker, ChatGPT Enterprise, and other frameworks within days; connect front-end to Teams/SharePoint and back-end to Databricks Lakehouse, AWS services, or other sources
- Build enterprise-grade Agentic RAG pipelines backed by Opensearch and other data stores; automate infrastructure with GitHub Actions; monitor latency, cost, adoption, and drift
- Collaborate with Data Engineering to design and maintain ETL pipelines, API integrations, and event-driven connectors feeding RAG and agents
- Orchestrate across AWS Bedrock, OpenAI, and Azure OpenAI behind secure connectors; package agents for seamless rollout
- Provide change enablement through training, feedback loops, iteration, and tracking adoption and ROI metrics; embed agents into daily routines and SOPs with a focus on behavior change KPIs
- Brief project leadership and clients on agent impact in business terms; contribute use cases and playbooks for Suffolk’s Construction Site of the Future
- Draft clear user stories, data specs, and acceptance criteria for complex solutions requiring central AI Solution Engineers or Data Engineering / Data Science teams; collaborate to scale prototypes into enterprise-ready solutions
- Ensure on-site AI tools meet Suffolk’s information security, data governance, and client confidentiality standards
Requirements
- 4–6+ years in AI engineering, full-stack data applications, or data science, including 2+ years building production LLM, RAG, or agentic solutions
- Bachelor’s degree in CS, Engineering, Physics, or a related field; Master’s preferred
- Hands-on experience in construction or heavy process industries (manufacturing, oil & gas, chemicals) is a significant plus
- Demonstrated process excellence background; Lean/Six Sigma Green Belt a huge plus; experience diagnosing process and data gaps and supporting change management with Operations Excellence
- Strong facilitation and communication skills
- Hands-on expertise with Claude Code, Devin AI, MS Copilot, and other coding assistants
- Programming and data stack: Python, SQL, FastAPI, Databricks Lakehouse, vector stores
- DevOps and IaC: GitHub Actions, Terraform or CloudFormation automation, or comparable CI/CD tooling; strong Git/GitHub workflow discipline
- Foundational ETL/ELT design knowledge, Airflow or Databricks Workflows, and REST/GraphQL 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
Technologies
- Python
- SQL
- FastAPI
- Databricks Lakehouse
- Databricks Workflows
- AWS Bedrock
- Amazon SageMaker
- ChatGPT Enterprise
- Microsoft Teams
- SharePoint
- OpenAI
- Azure OpenAI
- OpenSearch
- GitHub Actions
- Terraform
- CloudFormation
- Airflow
- REST
- GraphQL
- Vector stores
Working conditions
The role is performed on-site at active job sites and in an office environment with a quiet to moderate noise level. The position requires time on site, including occasional job site walking, and may involve travel.
