Key skills
AIGenAILLMOpenAIClaudeAnthropicRAGAgenticJupyterMLflowKubeflowETLELTSparkApache SparkAWSAzureKubernetesEKSSageMakerBedrockCommunicationCollaboration
About this role
Entagile is seeking a highly experienced Principal Gen AI Scientist to lead the design and development of innovative AI solutions. This role focuses on creating AI Agents and applications that address complex business challenges, requiring collaboration with cross-functional teams to implement enterprise-grade solutions.
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