About Suffolk

• Opportunity hunting and workflow redesign – Lead discovery; map value streams, assess process and data maturity, and log low-effort/high-impact AI use cases.
• Process and data maturity assessment – Evaluate 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 MVP agents in AWS Bedrock/Sagemaker, ChatGPT Enterprise, and other frameworks within days, with heavy use of AI coding assistants; wire them to Teams/SharePoint on the front end and Databricks Lakehouse , AWS services, or other sources on the back end.
• Enterprise-grade engineering & LLMOps – Build Agentic RAG pipelines backed by Opensearch and other data stores; automate infra with GitHub Actions; 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 AWS Bedrock, OpenA and Azure OpenAI 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 Suffolk’s “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. Work closely with those teams to turn prototypes into enterprise-ready scaled applications.
• Compliance – Ensure every on-site AI tool meets Suffolk’s information security, data governance, and client confidentiality standards.

• 4–6+ years in AI engineering / full-stack data applications or data science, including 2+ years building production LLM/RAG/Agentic 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 a huge plus) with experience diagnosing process and data gaps and supporting change management plans with Operations Excellence.
• Strong facilitation and communication skills.
• Hands-on expertise with Claude Code, Devin AI, MS Copilot, and other coding assistants
• Programming & data stack: Python, SQL, Fast API, Databricks Lakehouse, vector stores.
• DevOps & IaC: GitHub Actions Terraform or CloudFormation 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 pipelines.
• Willing and able to travel and work on active jobsites.