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.

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