Hi All
Local candidates only (must have valid DL) ; Maryland, Washington DC and Virgina
No GC candidates unless they have 15+ years experience + passport number.
In-person Interview
Title: Principal Gen AI Scientist
Location: Must be onsite in McLean, VA for 5 days a week (Monday to Friday)
Duration: Long Term
Interview Mode: In-Person interview
op Skills:
Machine Learning & Deep Learning – Required
GenAI – Required
Python - Required
Rag and/or Graph Rag – Required
MCP (Model Context Protocol) and A2A (Agent-to-Agent) is highly preferred
Supplier notes: Manager wants the resumes to be clean and easy to read. Please do not place large vendor summaries, manager will not read and may not consider your candidate.
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:**
* 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, 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
* 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.
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