Kanak Elite Services
Job Title
Data Scientist Specialist Location
McLean, Virginia 22102 (Fully 5 days onsite) Duration
06+ Months Contract Overview
Data Scientist Specialist with a focus on GenAI, RAG, Graph RAG, and AI agent-based solutions. The role requires hands-on experience in machine learning, prompt engineering, and scalable AI deployments in an enterprise environment. 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 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
MS/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, 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 GenAI system patterns and architectural best practices, Evaluation Frameworks Demonstrated ability to work in cross-functional agile teams. Github Code Repository Link required for each candidate; thorough vetting of 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. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: Software Development Note: This description consolidates the essential qualifications and responsibilities for the Data Scientist Specialist role and removes extraneous boilerplate.
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Data Scientist Specialist Location
McLean, Virginia 22102 (Fully 5 days onsite) Duration
06+ Months Contract Overview
Data Scientist Specialist with a focus on GenAI, RAG, Graph RAG, and AI agent-based solutions. The role requires hands-on experience in machine learning, prompt engineering, and scalable AI deployments in an enterprise environment. 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 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
MS/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, 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 GenAI system patterns and architectural best practices, Evaluation Frameworks Demonstrated ability to work in cross-functional agile teams. Github Code Repository Link required for each candidate; thorough vetting of 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. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: Software Development Note: This description consolidates the essential qualifications and responsibilities for the Data Scientist Specialist role and removes extraneous boilerplate.
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