- Define architecture of NLP and Generative AI solutions, including LLM integration, RAG pipelines, and multi-agent frameworks.
- Design & optimize retrieval systems using knowledge graphs and vector databases, improving contextual accuracy and semantic relevance in RAG workows.
- Implement of production-grade LLM and Agentic AI applications.
- Apply advanced techniques (e.g., document chunking strategies, rerankers, hybrid retrieval, query rewriting, feedback loops) to enhance RAG chain precision and reduce hallucinations.
- Collaborate with ontology/domain experts to integrate structured knowledge bases and semantic relationships into the solution stack.
- Leverage up-to-date frameworks like LangGraph, LangChain, LlamaIndex, SmolAgents,



and others for orchestrating agent-based and tool-augmented pipelines.
- Incorporate AWS Bedrock, Sagemaker, Azure ML Studio, Azure OpenAI Service, and Azure AI Foundry for cloud-native scalability and operational efficiency.
- Ensure high observability and maintainability of AI solutions through robust MLOps practices, logging, and model monitoring.
- Collaborate with product, cloud, software, and data engineering teams to deploy impactful AI capabilities in real-world settings.Role & responsibilities

Preferred candidate profile

📌 Senior AI Developer (Chennai)
🏢 Sunovaa Tech
📍 Chennai