About The Role

Experience with Programming (Python, SQL, Spark) and ML tools and cloud technologies (AWS, Azure, GCP).
Deep understanding of ML algorithms for common use cases in both structured and unstructured data systems.
Experience with GenAL/ LLM tools: LangChain, RAG pipelines, Embeddings & vector databases, Multi-modal LLMs, Fine-tuning techniques
Experience in building FastAPI-based services and Dockerized deployments
Experience building production-grade GenAI solutions
Familiarity with:
Multi-agent architectures and orchestration
Exposure to enterprise analytics / BI tools:
Power BI, Tableau, or other visualization tools

Experience collaborating with UI/UX teams for AI-driven solution delivery

Build and deploy enterprise-grade Generative AI and ML solutions
Design scalable AI systems enabling natural language–driven automation and insights
Develop and implement GenAI workflows for enterprise use cases
Build LLM-powered applications for:
Analytics & reporting
Document intelligence
Recommendation systems
Design and implement advanced RAG (Retrieval-Augmented Generation) pipelines including:
Embeddings
Vector search
Ranking and grounding mechanisms
Build and expose AI capabilities via REST APIs and microservices
Deploy, monitor, and manage solutions using Docker and cloud platforms
Implement output validation, monitoring, and evaluation frameworks for production reliability.
Strong understanding of AI system evaluation, monitoring, and guardrails
B.Tech / B.E. (Preferably from Tier-1 institutes)