Role: Gen-AI Engineer Responsibilities: • Lead the design and development of Gen-AI solutions, integrating them with business intelligence platforms to deliver new analytical capabilities • Collaborate with business stakeholders to translate Business needs into technical requirements. • Design and implement robust data models, including semantic layers and knowledge graphs, to establish clear relationships between structured and unstructured data. • Collaborate with Data Engineering team to define optimized and efficient pipelines for Metadata ingestion and Data Processing. • Develop and implement advanced prompt engineering strategies and Agentic RAG systems to ensure accurate, context-aware responses from Gen-AI models. • Design and develop Agentic AI systems to enable autonomous decision making tasks. • Monitor and evaluate model performance, ensuring continuous improvement in output quality, relevance, and user experience. • Ensure responsible and ethical AI development, including bias mitigation, transparency, and compliance with data governance policies. • Integrate Gen-AI models with existing BI tools, databases, and APIs to enable On-demand BI Reporting and conversational analytics. • Conduct research on emerging Gen-AI trends, especially in enterprise analytics, and implement innovative methodologies. • Documentation and version management of all Sprint Artefacts Technical Skills / Experience: Essential: • Expertise in Gen-AI model development, fine-tuning, prompt engineering, and developing RAG architecture. • Strong Python skills; experience with PyTorch or TensorFlow. • Strong proficiency in dimensional modeling (star/snowflake schemas), data warehousing concepts, and semantic layer design. • Ability to design, implement, and leverage knowledge graphs to represent complex data relationships using Neo4J/AWS Neptune • Hands-on experience with BI platforms (e.g., Tableau, Power BI) and their integration with AI systems. • Experience with web scraping libraries like BeautifulSoup, Scrapy, or Selenium. • Understanding of multi-agent systems and various communication architectures (e.g., MCP, Agent2Agent communication). • Experience with LLM orchestration, or agent-based frameworks like AutoGen, Crew AI etc. Desirable: • Working experience with Gen-AI cloud platforms such as AWS Bedrock/Databricks Mosaic AI. • Knowledge of MLOps and CI/CD for ML workflows. • Experience in Automobile Engineering Datasets. Behavioral • Excellent interpersonal skills • Amazing Team player and self-motivated individual • Creativity and ability to bring in innovative ideas for Kaizen and solving everyday problems