GenAI Engineer

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