Responsibilities
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, LangChain, 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.
DevOps & Platform Automation
- Use Terraform, AWS CDK, and GitHub 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.
Qualifications
- 5+ years of professional software development experience on AWS, with 2+ years focused on AI/ML engineering (LLMs, RAG, Bedrock, or similar). Strong coding proficiency in Python (LangChain, FastAPI, boto3) and 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, IaC (Terraform/CDK), and GitHub Actions
- Experience training, retraining and performing transfer learning on ML models desirable.
- Bachelor’s in Computer Science, 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.
- Excellent collaboration and communication skills — able to work cross-functionally but not dependent on business-side facilitation.
- 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.
Working Conditions
While performing the duties of this job, the employee is regularly required to sit for long periods of time; talk or hear; perform fine motor, hand and finger skills in the use of a keyboard, telephone, or writing. The employee is frequently required to stands; walk; and reach with arms and/or hands. Specific vision abilities include close vision, distance vision, depth perception and the ability to adjust focus. The employee will spend their time in an office environment with a quiet to moderate noise level. Job site walking.
Compensation Information
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.
