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 (India)
🏢 Sunovaa Tech
📍 India