Senior AWS Cloud & Generative AI Engineer

Overview:

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.