Data Scientist

Data Scientist Specialist

6 Months contract -Extension

Onsite McLean, VA

Interview Information:

Rounds: 1 Round -Interview Type: In Person

Must Have Qualifications:

• Data Science
• Machine Learning
• GenAI
• AI Agents
• RAG or Graph RAG
• Python - Jupyter
• MCP (Model Context Protocol)
• A2A (Agent2Agent)

**Key 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).
• 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 Knowledgebase/Elastic/Mongo Atlas) to support RAG architectures

**Required Qualifications:**

• PhD in AI/Data Science
• 10+ years of experience in AI/ML, with 3+ years in applied GenAI or LLM-based solutions.
• Deep expertise in prompt engineering, fine-tuning, RAG, Graph RAG, 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
• Demonstrated ability to work in cross-functional agile teams.
• Need Github Code Repository Link for each candidate. Please thoroughly vet the candidates.

**Preferred Qualifications:**

• Published contributions or patents in AI/ML/LLM domains.
• Hands-on experience with enterprise AI governance and ethical deployment frameworks.
• Familiarity with CI/CD practices for ML Ops and scalable inference APIs.