Key Responsibilities :

- Design and architect scalable data foundations for AWS SageMaker and Amazon Bedrock.

- Develop and implement Retrieval-Augmented Generation (RAG) architectures for enterprise AI applications.

- Work with Agent Core and develop effective integrations with Claude-based models.

- Design data pipelines and architectures to support AI/ML and Generative AI workloads.

- Identify opportunities to optimize and fine-tune AI/ML workloads for performance, scalability, and cost efficiency.

- Collaborate with Data Scientists, ML Engineers, Data Engineers, and Cloud Architects.

- Evaluate AI/ML solutions and recommend appropriate AWS services and architecture patterns.

- Ensure AI solutions meet requirements around reliability, security, scalability, and data quality.

Mandatory Skills :

- AWS SageMaker

- Amazon Bedrock

- RAG (Retrieval-Augmented Generation)

- Agent Core

- Claude / LLM Integration

- AI/ML Workload Optimization

- Data Architecture for AI/ML

Good to Have :

- Generative AI / LLM applications

- Vector databases and embeddings

- AWS data services

- MLOps / LLMOps

- Prompt engineering

- Model fine-tuning

- Strong understanding of cloud-native AI architectures

Candidate Profile :

- Strong experience designing AI/ML data and solution architectures.

- Hands-on experience with AWS AI/ML services.

- Strong understanding of RAG and enterprise GenAI architectures.

- Ability to optimize AI/ML workloads and work with emerging AI technologies.

- Excellent analytical, problem-solving, and stakeholder-management skills.