Note: "This role is open to W2 and C2C both"
Must Have Qualifications: Must have hands on experience with machine learning transitioned into GenAI. Rag, Python - Jupyter, other Software knowledge, using agents in workflows, strong understanding of data.
Preferred: Built AI agent, MCP, A2A, Graph Rag, deployed Gen AI applications to production.
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 AWS SageMaker and MLFlow/Kubeflow on Kubernetes (EKS).
- Collaborate with cross-functional teams of UI and microservice engineers, designers, and data engineers to build full-stack Gen AI experiences.
- Integrate GenAI solutions with enterprise platforms via API-based methods and GenAI standardized patterns.
- Establish and enforce validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails for production-ready deployment.
- Design & build robust ingestion pipelines that extract, chunk, enrich, and anonymize data from PDFs, video, and audio sources for use in LLM-powered workflows—leveraging best practices like semantic chunking and privacy controls.
- Orchestrate multimodal pipelines** using scalable frameworks (e.g., Apache Spark, PySpark) for automated ETL/ELT workflows appropriate for unstructured media.
- Implement embeddings drives—map media content to vector representations using embedding models, and integrate with vector stores (AWS Knowledge Base/Elastic/Mongo Atlas) to support RAG architectures.
Qualifications:
- BA or MS in AI/Data Science.
- Experience with AI/ML, with 3+ years in applied GenAI or LLM-based solutions.
- Deep expertise in prompt engineering, fine-tuning, RAG, GraphRAG, vector databases (e.g., AWS Knowledge Base / Elastic), and multi-modal models.
- Proven experience with cloud-native AI development (AWS SageMaker, Amazon 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.
- Demonstrated ability to work in cross-functional agile teams.
