AWS Cloud AI Engineer

Accountabilities

• Engineer, implement, and manage secure, scalable AI and machine learning platforms using AWS cloud technologies.
• Serve as a subject matter expert in AWS infrastructure optimization, applying Infrastructure as Code (IaC) practices to support highly available AI environments.
• Operationalize modern AI and Generative AI workloads, including large language model (LLM) applications, retrieval-augmented generation (RAG), and agent-based AI frameworks.
• Build and maintain cost-efficient cloud platforms using AWS-native services and automated CI/CD pipelines.
• Develop infrastructure solutions supporting AI services such as Amazon Bedrock, SageMaker, Kendra, and related AI capabilities.
• Create and maintain cloud automation, deployment workflows, and operational documentation.
• Collaborate with data scientists, developers, security teams, and platform engineers to move AI solutions from development into production.
• Support cloud modernization initiatives, including enterprise architecture improvements and workload migration strategies.
• Manage containerized AI applications using technologies such as Docker and Amazon EKS.
• Improve platform reliability through automation, proactive monitoring, and continuous optimization.

Requirements

• Experienced cloud and AI engineer with strong knowledge of AWS architecture, machine learning operations, and enterprise-scale technology environments.
• Proven experience designing, deploying, and supporting AI-driven workloads using AWS services such as Amazon SageMaker and Amazon Bedrock.
• Strong expertise in AWS foundational services including S3, EC2, RDS, VPC, KMS, and SNS.
• Experience developing and supporting Generative AI solutions using large language models, foundation models, and prompt engineering techniques.
• Knowledge of Retrieval-Augmented Generation (RAG), vector search solutions, and agentic AI workflows.
• Proficiency with Python-based machine learning frameworks such as Hugging Face, PyTorch, or TensorFlow.
• Hands-on experience with Infrastructure as Code tools such as Terraform or CloudFormation.
• Experience designing and managing CI/CD pipelines using tools such as GitHub Actions or AWS CodePipeline.
• Familiarity with cloud security, governance frameworks, compliance requirements, and regulated environments.
• Experience with containerization and orchestration technologies including Docker and Amazon EKS.
• Knowledge of monitoring and observability tools such as Amazon CloudWatch and CloudTrail.
• Ability to collaborate effectively with cross-functional teams and communicate complex technical concepts clearly.
• Strong analytical, troubleshooting, and problem-solving skills with a focus on automation and continuous improvement.
• Experience working in Agile environments and using tools such as Jira and Confluence.
• AWS certifications related to machine learning or Generative AI are preferred.
• Experience in healthcare or other highly regulated industries is a plus.