GenAI/AgenticAI consultant with AWS expertise-Full Time-Remote

We are seeking an experienced GenAI / Agentic AI Consultant with strong expertise in AWS Bedrock, AWS QuickSight, and AWS Cloud to design, develop, and deploy enterprise-grade Generative AI solutions. The ideal candidate should have hands-on experience building AI-powered applications using Large Language Models (LLMs), implementing Agentic AI workflows, integrating enterprise systems, and leveraging AWS services to deliver scalable, secure, and production-ready AI solutions.

Key Responsibilities

Generative AI & Agentic AI Development

• Design, develop, and deploy enterprise-grade Generative AI applications using AWS Bedrock.
• Build Agentic AI solutions capable of autonomous reasoning, planning, and task execution.
• Develop AI-powered chatbots, virtual assistants, and intelligent workflow automation.
• Implement Retrieval-Augmented Generation (RAG) architectures using vector databases.
• Design and optimize prompts to improve AI response quality and accuracy.
• Integrate foundation models such as Anthropic Claude, Amazon Nova, Meta Llama, and other Bedrock-supported models.

AWS Cloud Development

• Design and develop cloud-native AI applications using AWS services.
• Build serverless applications using AWS Lambda, API Gateway, and Step Functions.
• Develop secure REST APIs and backend services.
• Integrate AI solutions with enterprise applications using AWS SDKs and APIs.
• Implement secure authentication and authorization using AWS IAM and Cognito.
• Optimize cloud infrastructure for scalability, availability, and cost efficiency.

Data Engineering & AI Integration

• Integrate enterprise data sources including SQL, NoSQL, S3, APIs, and document repositories.
• Build document ingestion and knowledge management pipelines.
• Implement embeddings and semantic search using vector databases.
• Develop scalable AI workflows for document analysis, search, and recommendations.

Analytics & Reporting

• Build interactive dashboards and reports using AWS QuickSight.
• Create AI-powered analytics solutions for business users.
• Develop data visualizations and KPI dashboards.
• Integrate QuickSight with enterprise data sources and AI outputs.

DevOps & Deployment

• Containerize AI applications using Docker.
• Deploy workloads on Amazon ECS, EKS, or Kubernetes.
• Build CI/CD pipelines using AWS CodePipeline, CodeBuild, GitHub Actions, or Jenkins.
• Monitor AI applications using Amazon CloudWatch and AWS monitoring tools.