Senior AWS Cloud & Generative AI Engineer

Key Responsibilities:

• Design, develop, and implement Generative AI solutions leveraging Amazon Bedrock and related AWS services.

• Configure and optimize Amazon Bedrock Agent Orchestration for intelligent workflow and model lifecycle management.

• Apply Prompt Engineering methodologies for fine-tuning and optimizing AI model outputs.

• Implement Amazon Bedrock Guardrails to ensure compliance, brand safety, and responsible AI governance.

• Integrate and manage Amazon Knowledge Bases and Vector Databases to enhance contextual understanding and retrieval-augmented generation (RAG) workflows.

• Employ MLOps practices to automate model training, deployment, and monitoring within enterprise environments.

• Build and maintain CI/CD pipelines to support scalable and efficient deployment of AI and cloud-native applications.

• Utilize AWS services including CloudFormation, CDK, Step Functions, Lambda, EventBridge, DynamoDB, S3, Glue, and Athena to design and orchestrate robust cloud infrastructures.

• Develop infrastructure as code (IaC) solutions using AWS CDK and CloudFormation, ensuring version control and repeatable deployments.

• Collaborate with data scientists, solution architects, and DevOps teams to integrate AI capabilities with existing business systems.

• Create and maintain comprehensive technical documentation, including runbooks, design documents, and troubleshooting guides for deployed solutions.

• Ensure all solutions meet AWS best practices for security, compliance, scalability, and cost optimization.

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Required Skills & Experience:

• 10+ years of hands-on experience in AWS Cloud Computing within enterprise environments.

• Proven experience with Amazon Bedrock, including agent orchestration, workflow management, and model fine-tuning.

• Strong knowledge of Generative AI architectures, LLM integration, and Prompt Engineering techniques.

• Expertise in AWS Cloud Development Kit (CDK), CloudFormation, and Infrastructure as Code (IaC) principles.

• Proficiency with AWS services such as S3, Lambda, EventBridge, DynamoDB, Glue, Step Functions, and Athena.

• Experience implementing MLOps pipelines using AWS-native tools and frameworks.

• Solid understanding of CI/CD pipeline automation using AWS or third-party tools (e.g., CodePipeline, Jenkins, GitHub Actions).

• Familiarity with security and compliance standards for AI and cloud environments.

• Strong problem-solving, analytical, and communication skills, with a proven ability to work collaboratively across technical and non-technical teams.

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Preferred Qualifications:

• AWS Certified Solutions Architect / DevOps Engineer / Machine Learning Specialty certification.

• Experience deploying Retrieval-Augmented Generation (RAG) systems using AWS Bedrock and vector search technologies.

• Prior experience integrating LLMs (e.g., Anthropic Claude, Amazon Titan, or other Bedrock models) into enterprise solutions.

• Working knowledge of Python, TypeScript, or Java for automation and API integration.