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 .

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.

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.