AI Engineer
Suffolk Construction2 years ago
Boston, MA, USAMid Level / Senior
Responsibilities
- 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 and multi-agent systems that select and orchestrate LLMs based on task context, cost, and latency.
- Own the full lifecycle of AI solutions, including design, development, testing, deployment, monitoring, and maintenance.
- Build APIs, backend services, agentic workflows, reusable connectors, and orchestration layers using Python, FastAPI, LangChain, and AWS SDKs.
- Develop Teams and web SPA integrations through API endpoints and support enterprise data integration with ETL/ELT pipelines.
- Automate infrastructure and deployments using Terraform, AWS CDK, and GitHub Actions.
- Implement LLMOps practices including cost monitoring, latency optimization, usage analytics, and model versioning.
- Enforce security, governance, and access standards and improve data quality, observability, caching, and vectorization.
- Collaborate with product managers, site AI engineers, data scientists, and Data Engineering while communicating progress to non-technical stakeholders.
Requirements
- Require 4–6 years of professional software development experience on AWS, including 2+ years focused on AI/ML engineering involving LLMs, RAG, Bedrock, or similar.
- Require strong Python coding proficiency with LangChain, FastAPI, and boto3, plus solid experience with SQL, Databricks, and vector databases.
- Require experience deploying production systems with AWS Lambda, ECS/EKS, API Gateway, Step Functions, S3, CloudFront, and KMS.
- Require a strong foundation in CI/CD, infrastructure as code using Terraform or AWS CDK, and GitHub Actions.
- Require foundational ETL/ELT knowledge, Airflow or Databricks Workflows experience, and REST/GraphQL API development.
- Require a bachelor’s degree in Computer Science, Engineering, Physics, or a related field; a master’s degree is preferred.
- Experience training, retraining, and transfer learning on ML models is desirable.
- Hands-on construction or heavy-process-industry experience is a significant plus.
- Excellent collaboration and communication skills are required for cross-functional work.
Benefits
- Competitive salaries and, for certain roles, auto allowances and gas cards.
- Medical, dental, vision, virtual care, and emotional and mental health benefits.
- Generous paid time off, a 401(k) plan with employer match, and financial resources.
- Company-paid and voluntary life insurance, tax-deferred savings accounts, short- and long-term disability, commuter benefits, and 10 backup daycare days annually.
- The role is based in an office environment with job-site walking and regular sitting, standing, walking, and keyboard or telephone use.