Data Scientist Specialist

IN-PERSON INTERVIEW

Need locals

Job Title: Data Scientist Specialist

Location: McLean Virginia 22102 (Fully 5 days onsite)

Duration: 06 Months Contract

Need Local Candidates In-person interview is required

Supplier Vetting Questions:

1. Assessment testing on the must have listed is required for candidates to be considered.
2. What is RAG and how have you implemented it
3. What is Semantic Similarity Search and how can this be done
4. What is the formula for Precision

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.

Required Qualifications:

• MS/PhD in AI/Data Science
• 10 plus years of experience in AI/ML with 3 plus years in applied GenAI or LLM based solutions.
• Deep expertise in prompt engineering finetuning RAG GraphRAG vector databases (e.g. AWS Knowledge Base / 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.
• Handson experience with enterprise AI governance and ethical deployment frameworks.
• Familiarity with CI/CD practices for ML Ops and scalable inference APIs.

Position Summary:

• Client is seeking a highly experienced Principal Gen AI Scientist with a strong focus on Generative AI (GenAI) to lead the design and development of cutting-edge AI Agents Agentic Workflows and Gen AI Applications that solve complex business problems.
• This role requires advanced proficiency in Prompt Engineering Large Language Models (LLMs) RAG Graph RAG MCP A2A multimodal AI Gen AI Patterns Evaluation Frameworks Guardrails data curation and AWS cloud deployments.
• Candidates will serve as a hands-on Gen AI (data) scientist and critical thought leader working alongside full stack developers UX designers product managers and data engineers to shape and implement enterprise-grade Gen AI solutions.

Key Responsibilities:

• Architect and implement scalable AI Agents Agentic Workflows and GenAI applications to address diverse and complex business use cases.
• Develop finetune 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 Bedrocks Knowledge Base/Elastic
• Implement solutions leveraging MCP (Model Context Protocol) and A2A (Agentto-Agent) communication.
• Build and maintain Jupyterbased 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 APIbased methods and GenAI standardized patterns.
• Establish and enforce validation procedures with Evaluation Frameworks bias mitigation safety protocols and guardrails for productionready deployment.
• Design & build robust ingestion pipelines that extract chunk enrich and anonymize data from PDFs video and audio sources for use in LLMpowered 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.

Preferred:

Built AI agent MCP A2A Graph Rag deployed Gen AI applications to production

Feel free to reach me at