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POSITION OVERVIEW:

We are seeking an Agile Development Professional - Gen AI (Data) Scientist with a strong focus on Generative AI (Gen AI) 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.

QUALIFICATIONS:

• Bachelor’s in data science/computer science with Machine Learning focus, Business Analytics. Advanced studies/degree preferred
• 2-4 years of experience in Data Science, Computer Science with focus on Machine Learning and Business Analytics
• At least 2 years in applied Gen AI or LLM-based solutions preferred
• Experience training and testing Machine/Deep Learning, Natural Language Models
• Proven experience with AI development on AWS SageMaker, Bedrock, ML Flow on EKS
• Strong programming skills in Python and ML libraries (Transformers, Lang Chain, etc.)
• Deep understanding of Gen AI system patterns and architectural best practices, Evaluation Frameworks
• Demonstrated ability to work in cross-functional agile teams
• Deep expertise in prompt engineering, fine-tuning, RAG, Graph RAG, vector databases (e.g., AWS Knowledge Base / Elastic), and multi-modal models.
• 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.

KEY RESPONSIBILITIES:

• Design and implement scalable AI Agents, Agentic Workflows and GenAI applications to address diverse and complex business use cases.
• Evaluate and adapt models such as Claude (Anthropic), Azure OpenAI, and open-source alternatives for business use cases
• Train, Fine-tune, optimize and Test lightweight Large Language Models (LLMs) to address diverse and complex business use cases
• 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 Knowledge Base/Elastic/Mongo Atlas) to support RAG architectures

KEYS TO SUCCESS IN THIS ROLE:

• Deep Gen AI Expertise: Master generative AI technologies, including LLMs and multi-modal AI, to design innovative solutions.
• Prompt Engineering Proficiency: Excel in crafting effective prompts for optimizing AI model interactions.
• Analytical Problem-Solving: Apply strong analytical skills to adapt AI models for complex business challenges.
• Collaborative Teamwork: Communicate effectively with cross-functional teams to integrate AI solutions seamlessly.
• AWS Cloud Deployment Skills: Utilize AWS platforms like SageMaker for scalable AI application deployment.
• AI Governance and Ethics: Implement robust validation procedures to ensure ethical and compliant AI solutions.
• Continuous Learning: Stay adaptable and keep up with the latest AI advancements.
• Scalable Solution Development: Focus on creating scalable AI agents and workflows for diverse use cases.
• API Integration: Develop skills in API-based integration for seamless deployment within enterprise systems.
• Data Management Expertise: Curate and manage enterprise data for robust AI development.