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.

Where required by law, pay ranges can be found in Suffolk's job postings. Base Salary for this position is just one component of Suffolk’s total rewards package for employees. Actual salaries may be based on several factors including, but not limited to, a candidate's skill set, experience, education and other qualifications. Suffolk offers a comprehensive benefits package as part of its overall total rewards strategy. Salary ranges are reviewed regularly to reflect market trends.