Lead the design and development of cutting-edge AI Agents, Agentic Workflows and Gen AI Applications that solve complex business problems.
Requirements
- 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
- 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.
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).
- Integrate GenAI solutions with enterprise platforms via API-based methods and GenAI standardized patterns.
Other
- PhD in AI/Data Science
- 10+ years of experience in AI/ML, with 3+ years in applied GenAI or LLM-based solutions.
- Demonstrated ability to work in cross-functional agile teams.
- Need Github Code Repository Link for each candidate. Please thoroughly vet the candidates.
- This is a fully onsite position in Mc Lean,VA.
