AI Engineer

Overview

About Suffolk

Suffolk is a national enterprise that builds, innovates, and invests. We provide value across the entire project lifecycle through our core construction management services and complementary business lines in real estate investment, design, self-perform construction, and technology start-up investment (Suffolk Technologies). By integrating data, artificial intelligence, and advanced technology through our Seamless Platform, we connect design, construction, and operations to deliver smarter, more predictable results and redefine how America builds.

Suffolk – America’s Contractor – is a national company with more than $9 billion in annual revenue, 3,000 employees, and 17 offices, including Boston (headquarters), New York City, Miami, West Palm Beach, Tampa, Estero, Dallas, Los Angeles, San Francisco, San Diego, Las Vegas, Herndon, U.S. Virgin Islands, and other key markets. Suffolk manages some of the most complex and transformative projects in the country, serving clients across healthcare, life sciences, education, gaming, aviation, transportation, government, mission critical, and commercial sectors.

Suffolk is privately held and is led by founder, chairman and CEO John Fish. Suffolk is ranked #8 on ENR’s list of "Top CM-at-Risk Contractors." For more information, visit  and follow Suffolk on Facebook, Twitter, Linked In, You Tube, and Instagram.

At Suffolk, we believe that our total rewards program should offer you and your family the support you need when it matters most. That’s why we have created a program that provides employees with access to a wide variety of options that can be personalized to support you and your loved ones physically, emotionally, and financially.

Benefits include, competitive salaries, auto allowances and gas cards for certain roles, access to market leading medical and emotional and mental health benefits, dental, and vision insurance plans, virtual care options for physical therapy and primary care, generous paid time off, 401k plan with employer match and access to expert financial resources, company paid and voluntary life insurance, tax deferred savings accounts, 10 backup daycare days each year, short- and long-term disability, commuter benefits and more.

For more information, clickhere.

AI Product Engineering & Deployment

• Translate product requirements and user stories into production-grade AI solutions using AWS Bedrock, Lambda, ECS/EKS, and Databricks.
• Implement RAG pipelines with Delta tables, Unity Catalog, and Vector Search.
• Design and deploy multi-model agents that dynamically select between LLMs (Claude, GPT, Llama, Titan, etc.) based on task context, cost, and latency.
• Implement multi-agent orchestration frameworks enabling collaboration among specialized agents (e.g., data retriever, planner, summarizer, and action executor) for complex construction workflows.
• Own full lifecycle delivery — design, development, testing, deployment, monitoring, and maintenance.

Full-Stack & Backend Development

• Build APIs, backend services, and agentic workflows using Python, FastAPI, Lang Chain, and AWS SDKs.
• Create reusable connectors and orchestration layers for multi-model agents (Claude, GPT, Llama, etc.).
• Develop front-end integrations for Teams and web SPAs via REST or GraphQL endpoints.

Data Engineering & Integration

• Partner with Data Engineering to design robust ETL/ELT pipelines from enterprise systems to the Databricks Lakehouse.
• Ensure efficient data access, caching, and vectorization for low-latency AI response.
• Build tools to monitor and improve data quality, latency, and observability.

Dev Ops & Platform Automation

• Use Terraform, AWS CDK, and Git Hub Actions to automate infrastructure and deployments.
• Implement LLMOps: cost monitoring, latency optimization, usage analytics, and model versioning.
• Enforce security, governance, and access standards in line with enterprise policies.

Collaboration & Communication

• Work closely with product managers, site AI engineers, and data scientists to iterate rapidly in Agile sprints.
• Communicate technical progress clearly to non-technical stakeholders; contribute to internal AI playbooks and templates.