Hi ,
Our client is looking AWS GenAI Senior For a Contract Position role New York, NY (Hybrid) b elow is the detailed requirements.
Kindly share your Updated Resume to proceed further.
Position : AWS GenAI Senior
Location : Whippany NJ (Hybrid) Day one Onsite
Job Mode : Long Term Contract
Summary : We are seeking an experienced AWS Generative AI expert to design and implement cutting-edge AI solutions using AWS's comprehensive AI services . The ideal candidate will combine deep technical expertise in AWS GenAI services with strong development skills and thorough understanding of LLMs, vector databases , AI Agents, and MCP (Model Context Protocol).
Skills Required :
- Experience 5+ years Python development , 3+ years AWS services Core Expertise : AWS Bedrock, foundation models, and AI Agents architecture
- Model Context Protocol (MCP) implementation and integration
- Vector databases, embedding models, and semantic search
- OpenSearch configuration and RAG implementations
- Serverless architectures ( Lambda, ECS, Step Functions )
- RDS PostgreSQL , Secrets Manager, CloudWatch monitoring
- Container orchestration ( ECS / EKS) and auto-scaling
- AI Guardrails, PII detection, and security best practices
Essential Knowledge Areas
Advanced prompt engineering and multi-agent conversation flowsLLM capabilities , limitations, and MCP protocol standardsAI security, governance, and responsible AI principlesModel evaluation metrics and performance optimizationAI cost optimization and deployment architecturesData pre-processing pipelines and compliance requirementsKey Soft Skills
Strong problem-solving and analytical abilitiesExcellent communication skills for technical and executive audiencesLeadership capabilities for AI initiatives and team mentoringAgile environment experience with strong project management skillsStrategic thinking and change management abilities"Core Responsibilities
Design end-to-end GenAI solutions using AWS Bedrock with foundation models (Claude, Titan, etc.)Develop and optimize AI Agents and multi-agent systems with MCP integrationImplement RAG (Retrieval-Augmented Generation) and vector search solutions using OpenSearchBuild Serverless AI architectures using Lambda, ECS, and Step FunctionsCreate RESTful APIs and microservices for AI application integrationDevelop NLP pipelines using AWS Comprehend & GuardrailsImplement Infrastructure as Code using CloudFormationLead POC development and cost optimization strategies for AI solutionsBuild automated model evaluation and monitoring frameworks