Senior AI Engineer

Suffolk Construction • Boston, MA

20h

About The Position

Suffolk is a national enterprise that builds, innovates, and invests. Its services span construction management, real estate investment, design, self-perform construction, and technology start-up investment. Through its Seamless Platform, Suffolk integrates data, artificial intelligence, and advanced technology across design, construction, and operations to deliver smarter, more predictable results.

The Senior AI Engineer translates product requirements into production-grade AI solutions and owns their lifecycle from design and development through testing, deployment, monitoring, and maintenance. The role builds retrieval-augmented generation pipelines and multi-model, multi-agent systems for construction workflows, along with backend services, APIs, and integrations for web applications and Microsoft Teams. It also partners with Data Engineering to connect enterprise systems to the Databricks Lakehouse, improve data quality and performance, and automates infrastructure and deployment while implementing LLM operations, security, and governance.

The position works closely with product managers, site AI engineers, and data scientists in Agile sprints, and communicates progress to both technical and non-technical stakeholders. The work is performed in an office environment with quiet to moderate noise, and includes job-site walking. Suffolk describes itself as having more than $13 billion in annual revenue, 3,000 employees, and 17 offices.

Requirements

• At least 5 years of professional software development experience on AWS, including at least 2 years focused on AI/ML engineering such as LLMs, RAG, or Bedrock.
• Strong Python proficiency, including LangChain, FastAPI, and boto3.
• Solid experience with SQL, Databricks, and vector databases.
• Experience designing and deploying production systems using AWS Lambda, ECS/EKS, API Gateway, Step Functions, S3, CloudFront, and KMS.
• Strong foundation in CI/CD, infrastructure as code using Terraform or CDK, and GitHub Actions.
• Bachelor’s degree in Computer Science, Engineering, Physics, or a related field.
• Excellent collaboration and communication skills, with the ability to work cross-functionally without depending on business-side facilitation.
• 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.

Nice To Haves

• Master’s degree.
• Experience training, retraining, and performing transfer learning on ML models.
• Hands-on experience in construction or heavy process industries such as manufacturing, oil and gas, or chemicals; this is described as a significant plus.

Responsibilities

• Translate product requirements and user stories into production-grade AI solutions using AWS Bedrock, Lambda, ECS/EKS, and Databricks.
• Implement RAG pipelines using Delta tables, Unity Catalog, and Vector Search.
• Design and deploy multi-model agents that select LLMs based on task context, cost, and latency.
• Implement multi-agent orchestration for complex construction workflows, coordinating specialized agents such as retrievers, planners, summarizers, and action executors.
• Own full-lifecycle delivery, including design, development, testing, deployment, monitoring, and maintenance.
• Build APIs, backend services, and agentic workflows using Python, FastAPI, LangChain, and AWS SDKs.
• Create reusable connectors and orchestration layers for multi-model agents.
• Develop front-end integrations for Teams and web single-page applications through REST or GraphQL endpoints.
• Partner with Data Engineering to design ETL/ELT pipelines from enterprise systems to the Databricks Lakehouse.
• Improve efficient data access, caching, and vectorization to support low-latency AI responses.
• Build tools to monitor and improve data quality, latency, and observability.
• Automate infrastructure and deployments using Terraform, AWS CDK, and GitHub Actions.
• Implement LLM operations, including cost monitoring, latency optimization, usage analytics, and model versioning.
• Enforce security, governance, and access standards consistent with enterprise policies.
• Collaborate with product managers, site AI engineers, and data scientists in Agile sprints.
• Communicate technical progress to non-technical stakeholders and contribute to internal AI playbooks and templates.

Benefits

• Competitive salaries.
• Auto allowances and gas cards for certain roles.
• 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.
• 401(k) 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior