Zenius Corporation
Overview
Join to apply for the
Principal GenAI Data Scientist
role at
Zenius Corporation . Duration: 3 months; Start: ASAP; Location: Onsite. We are seeking a highly experienced Principal GenAI Data Scientist to lead the design and development of AI agents, agentic workflows, and production GenAI applications that solve real business problems. You will be a hands-on technical leader who partners with full-stack engineers, designers, product managers, and data engineers to ship secure, reliable, and scalable GenAI solutions. Responsibilities
Architect & build AI agents, agentic workflows, and end-to-end GenAI apps for diverse enterprise use cases. Develop, fine-tune, and evaluate LLMs (e.g., Claude/Anthropic, Azure OpenAI, and OSS) and select the right model per use case (cost/latency/quality). Design & deploy RAG and Graph-RAG systems using vector stores and knowledge bases; implement semantic chunking, metadata enrichment, and privacy controls. Implement embeddings pipelines and integrate with vector stores (e.g., AWS Knowledge Bases/Bedrock, Elastic, MongoDB Atlas). Leverage MCP (Model Context Protocol) and A2A (agent-to-agent) patterns to compose multi-agent solutions. Build notebooks & services in Python/Jupyter; use SageMaker and MLFlow/Kubeflow on EKS for training, tracking, and deployment. Curate enterprise data via connectors; orchestrate multimodal ETL/ELT (PDF/audio/video) with Spark/PySpark. Integrate GenAI capabilities into enterprise platforms via APIs and standardized GenAI patterns. Establish evaluation & safety: define automatic evals, bias testing, guardrails, and deployment readiness criteria. Collaborate cross-functionally with UI/microservices teams to deliver polished, production solutions and measurable business value. Document & mentor: codify patterns, playbooks, and best practices for repeatable delivery. Must-Have Qualifications
Hands-on ML to GenAI transition with demonstrated delivery of AI agents/agentic workflows. Deep experience with RAG (documents to vectors, retrieval, synthesis) and Graph-RAG. Strong Python (Jupyter) and modern ML stack (Transformers, LangChain/LlamaIndex or similar). Practical use of MCP and A2A communication patterns in real workflows. Cloud-native AI on AWS (SageMaker, Bedrock; MLFlow/Kubeflow on EKS). Vector databases/knowledge bases (AWS Knowledge Bases/Bedrock, Elastic, MongoDB Atlas, etc.). Proven prompt engineering, fine-tuning, evaluation frameworks, and guardrails/safety implementation. Built and deployed GenAI apps to production (latency, cost, observability, rollback, safety). Strong data engineering fundamentals: ingestion, chunking, enrichment, anonymization, and governance. Required Experience
10+ years in AI/ML with 3+ years focused on applied GenAI/LLM solutions. Prior software engineering experience and ability to partner closely with full-stack teams. GitHub repository link required for consideration (please include recent GenAI/agent work). Preferred Qualifications
Publications or patents in AI/ML/LLM. Experience with enterprise AI governance and ethical deployment. CI/CD for MLOps and scalable inference APIs; observability and evaluation in production. Experience designing business use cases from problem framing through measurable outcomes. Nice to Have (Role-Aligned Extras)
Multi-modal models (text/image/audio/video) and tool-use/function-calling. Knowledge graphs for Graph-RAG; retrieval policy and query planning. GenAI architectural patterns (routing, orchestration, distillation, hybrid search). Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: IT Services and IT Consulting Referrals increase your chances of interviewing at Zenius Corporation by 2x. Get notified about new Data Scientist jobs in
McLean, VA . Salary ranges shown in this listing vary by location and may be updated over time.
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Join to apply for the
Principal GenAI Data Scientist
role at
Zenius Corporation . Duration: 3 months; Start: ASAP; Location: Onsite. We are seeking a highly experienced Principal GenAI Data Scientist to lead the design and development of AI agents, agentic workflows, and production GenAI applications that solve real business problems. You will be a hands-on technical leader who partners with full-stack engineers, designers, product managers, and data engineers to ship secure, reliable, and scalable GenAI solutions. Responsibilities
Architect & build AI agents, agentic workflows, and end-to-end GenAI apps for diverse enterprise use cases. Develop, fine-tune, and evaluate LLMs (e.g., Claude/Anthropic, Azure OpenAI, and OSS) and select the right model per use case (cost/latency/quality). Design & deploy RAG and Graph-RAG systems using vector stores and knowledge bases; implement semantic chunking, metadata enrichment, and privacy controls. Implement embeddings pipelines and integrate with vector stores (e.g., AWS Knowledge Bases/Bedrock, Elastic, MongoDB Atlas). Leverage MCP (Model Context Protocol) and A2A (agent-to-agent) patterns to compose multi-agent solutions. Build notebooks & services in Python/Jupyter; use SageMaker and MLFlow/Kubeflow on EKS for training, tracking, and deployment. Curate enterprise data via connectors; orchestrate multimodal ETL/ELT (PDF/audio/video) with Spark/PySpark. Integrate GenAI capabilities into enterprise platforms via APIs and standardized GenAI patterns. Establish evaluation & safety: define automatic evals, bias testing, guardrails, and deployment readiness criteria. Collaborate cross-functionally with UI/microservices teams to deliver polished, production solutions and measurable business value. Document & mentor: codify patterns, playbooks, and best practices for repeatable delivery. Must-Have Qualifications
Hands-on ML to GenAI transition with demonstrated delivery of AI agents/agentic workflows. Deep experience with RAG (documents to vectors, retrieval, synthesis) and Graph-RAG. Strong Python (Jupyter) and modern ML stack (Transformers, LangChain/LlamaIndex or similar). Practical use of MCP and A2A communication patterns in real workflows. Cloud-native AI on AWS (SageMaker, Bedrock; MLFlow/Kubeflow on EKS). Vector databases/knowledge bases (AWS Knowledge Bases/Bedrock, Elastic, MongoDB Atlas, etc.). Proven prompt engineering, fine-tuning, evaluation frameworks, and guardrails/safety implementation. Built and deployed GenAI apps to production (latency, cost, observability, rollback, safety). Strong data engineering fundamentals: ingestion, chunking, enrichment, anonymization, and governance. Required Experience
10+ years in AI/ML with 3+ years focused on applied GenAI/LLM solutions. Prior software engineering experience and ability to partner closely with full-stack teams. GitHub repository link required for consideration (please include recent GenAI/agent work). Preferred Qualifications
Publications or patents in AI/ML/LLM. Experience with enterprise AI governance and ethical deployment. CI/CD for MLOps and scalable inference APIs; observability and evaluation in production. Experience designing business use cases from problem framing through measurable outcomes. Nice to Have (Role-Aligned Extras)
Multi-modal models (text/image/audio/video) and tool-use/function-calling. Knowledge graphs for Graph-RAG; retrieval policy and query planning. GenAI architectural patterns (routing, orchestration, distillation, hybrid search). Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: IT Services and IT Consulting Referrals increase your chances of interviewing at Zenius Corporation by 2x. Get notified about new Data Scientist jobs in
McLean, VA . Salary ranges shown in this listing vary by location and may be updated over time.
#J-18808-Ljbffr