Overview
On Site
$85 - $98
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 6 Month(s)
No Travel Required
Able to Provide Sponsorship
Skills
Generative Artificial Intelligence (AI)
LangChain
Python
PySpark
Extract
Transform
Load
Cloud Computing
Artificial Intelligence
LLM
Machine Learning
Prompt Engineering
RAG
Graph RAG
MCP
A2A
multi-modal AI
Gen AI Patterns
Evaluation Frameworks
data curation
AWS cloud deployments
Job Details
Principal Gen AI Scientist/Data Scientist
Duration: 6 Months (possible extension or conversion)
Location: Mc Lean, VA 22102 ONSITE REQUIREMENT 5 days onsite
Role in Short:
We are 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, multi-modal AI, Gen AI Patterns, Evaluation Frameworks, Guardrails, data curation, and AWS cloud deployments. You 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, 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, 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.
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. Preferred: Built AI agent, MCP, A2A, Graph Rag, deployed Gen AI applications to production.
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.
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
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