AWS Cloud AI Engineer

POSITION SUMMARY

The AWS Cloud AI Engineer 2 at Boston Medical Center (BMC) is responsible for the engineering, implementation, and operational management of secure, scalable AI/ML platforms on Amazon Web Services. This position serves as a Subject Matter Expert (SME) in optimizing the underlying AWS ecosystem, leveraging Infrastructure as Code (IaC) and advanced monitoring to ensure model endpoints and data planes remain highly available.

Beyond core cloud engineering, the role focuses on the end-to-end operationalization of modern AI and Generative AI workloads. Responsibilities include architecting the infrastructure guardrails necessary for high-performance environments such as Amazon Bedrock, Sage Maker, and Kendra while maintaining strict adherence to enterprise security and governance standards. The ideal candidate will bring strong expertise in AWS architecture, infrastructure automation, Dev Ops practices, and AI platform integration, along with excellent communication skills and the ability to build strong working relationships across technical and business teams.

Position: AWS Cloud AI Engineer Department: ITS Network - Tech Support

Schedule:

Full Time

The AWS AI Engineer 2 at Boston Medical Center (BMC) is responsible for the following tasks:
Engineer, implement, and manage secure, scalable AI/ML platforms specifically within the AWS ecosystem. Serve as a Subject Matter Expert (SME) in optimizing AWS infrastructure using Infrastructure as Code (IaC) to ensure high availability for model endpoints and data planes. Lead the end-to-end operationalization of modern AI and Generative AI workloads, including LLM-powered applications, Retrieval-Augmented Generation (RAG), and Agentic AI frameworks.

Build and maintain reliable, cost-efficient platforms utilizing native AWS services and automated CI/CD pipelines to transition intelligent solutions from development to production. Implement advanced monitoring solutions to oversee platform health, performance, and the stability of AI-driven workloads. Act as a technical lead to advance the organization’s cloud maturity, ensuring all AWS-based AI solutions are robust, secure, and "AI-ready."

Bachelor’s degree in Computer Science, Engineering, or related discipline with at least 5 years of experience in IT Systems Engineering or equivalent combination of education and experience. Demonstrated familiarity with deploying and operationalizing AI-driven workloads, specifically utilizing services like Amazon Sage Maker or Amazon Bedrock. Healthcare domain knowledge and working in regulated environments is a plus (HIPAA, HITRUST, SOC2)

Master’s degree in Computer Science with a minimum of 5 years of dedicated expertise in engineering and operating enterprise-scale environments exclusively on AWS. 3 years of hands‑on experience managing foundational AWS services (S3, EC2, RDS, VPC, KMS, SNS).

Preferred: AWS

Certifications:

AWS certified Machine Learning Engineer or AWS certified Generative AI Developer

Proven experience building and supporting Generative AI solutions, including the integration of Large Language Models (LLMs), foundation models, and the application of advanced prompt engineering techniques to optimize application workflows. Familiarity with Retrieval-Augmented Generation (RAG) and Agentic AI frameworks, specifically orchestrating multi-step reasoning workflows and integrating LLMs with enterprise vector search capabilities. Deep technical proficiency within the AWS AI/ML ecosystem, specifically leveraging Amazon Bedrock, Sage Maker, Kendra, and specialized services such as Comprehend, Rekognition, or Lex.

Proficiency in Python-based machine learning frameworks such as Hugging Face, PyTorch, or Tensor Flow to support the development and deployment of intelligent applications. Demonstrated ability to collaborate with data scientists, developers, and platform teams to transition experimental AI/ML workloads into production-ready, enterprise-grade cloud environments.…