AWS Cloud AI Engineer
About The Position
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, SageMaker, and Kendra while maintaining strict adherence to enterprise security and governance standards. The ideal candidate will bring strong expertise in AWS architecture, infrastructure automation, DevOps practices, and AI platform integration, along with excellent communication skills and the ability to build strong working relationships across technical and business teams.
Requirements
- 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 SageMaker or Amazon Bedrock.
- 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, SageMaker, Kendra, and specialized services such as Comprehend, Rekognition, or Lex.
- Proficiency in Python-based machine learning frameworks such as Hugging Face, PyTorch, or TensorFlow 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.
- Experience implementing Infrastructure as Code (IaC) using Terraform or CloudFormation to provision and manage high-performance environments tailored for AI and LLM-powered workloads.
- Experience designing and managing CI/CD pipelines (e.g., GitHub Actions, AWS CodePipeline) focused on the continuous integration and delivery of AI models and automated agentic workflows.
- Proficiency in building asynchronous, event-driven architectures for AI processing using AWS Lambda and modern integration patterns.
- Experience leveraging Docker and Amazon EKS to orchestrate containerized AI microservices and scalable inference endpoints.
- Knowledge of monitoring and observability tools, including Amazon CloudWatch and CloudTrail, to ensure the health and performance of AI model endpoints and data planes.
- Ability to embed security, compliance, and governance controls directly into AI infrastructure automation and delivery pipelines.
- Familiarity with enterprise cloud strategy, including multi-account architectures and the assessment of workloads for cloud migration or modernization initiatives.
- Experience working within Agile environments, maintaining technical documentation and operational runbooks using tools such as Jira and Confluence.
- Strong analytical and troubleshooting skills with a consistent focus on automation, reliability, and the continuous improvement of the AI ecosystem.
Nice To Haves
- 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).
- AWS Certifications: AWS certified Machine Learning Engineer or AWS certified Generative AI Developer
Responsibilities
- 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."
Benefits
- medical, dental, vision, pharmacy
- discretionary annual bonuses and merit increases
- Flexible Spending Accounts
- 403(b) savings matches
- paid time off
- career advancement opportunities
- resources to support employee and family well-being
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
