Solving complex business problems using Generative AI (GenAI) and designing cutting-edge AI Agents, Agentic Workflows, and Gen AI Applications
Requirements
- Hands-on experience with machine learning transitioned into GenAI
- Rag, Python- Jupyter, other Software knowledge, using agents in workflows, strong understanding of data
- Deep expertise in prompt engineering, fine-tuning, RAG, GraphRAG, vector databases (e.g., AWS KnowledgeBase / Elastic), and multi-modal models
- Proven experience with cloud-native AI development (AWS SageMaker, Bedrock, MLFlow on EKS)
- Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.)
- Deep understanding of Gen AI system patterns and architectural best practices, Evaluation Frameworks
- Familiarity with CI/CD practices for ML Ops and scalable inference APIs
Responsibilities
- Architect and implement scalable AI Agents, Agentic Workflows and GenAI applications to address diverse and complex business use cases
- Develop, fine-tune, and optimize lightweight LLMs; lead the evaluation and adaptation of models such as Claude (Anthropic), Azure OpenAI, and open-source alternatives
- Design and deploy Retrieval-Augmented Generation (RAG) and Graph RAG systems using vector databases and knowledge bases
- Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge Base/Elastic
- Implement solutions leveraging MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication
- Build and maintain Jupyter-based notebooks using platforms like SageMaker and MLFlow/Kubeflow on Kubernetes (EKS)
- Integrate GenAI solutions with enterprise platforms via API-based methods and GenAI standardized patterns
Other
- PhD in AI/Data Science
- 10+ years of experience in AI/ML, with 3+ years in applied GenAI or LLM-based solutions
- Demonstrated ability to work in cross-functional agile teams
- Need Github Code Repository Link for each candidate
- Full-time on-site M-F
