Responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures. - Responsible for building out sophisticated AI agent workflows, establishing a scalable cloud infrastructure, and ensuring the application can handle large- scale document ingestion and concurrent user requests seamlessly. - Design and implement highly complex Retrieval-Augmented Generation (RAG) systems, specifically focusing on Agentic RAG (autonomous decision-making workflows) and Multimodal RAG (handling text, images, and other data types).

Backend Development & Orchestration: Build robust, high-performance APIs and backend services using Python and FastAPI. Orchestrate complex LLM workflows and agents utilizing LangChain and LangGraph. - Search & Data Infrastructure: Develop data ingestion pipelines capable of handling exceptionally large documents.

Configure, optimize, and maintain Vector Databases and Azure AI Search for low-latency, high-accuracy retrieval - Frontend & UI Development: Develop rapid, functional, and interactive user interfaces using Streamlit.

Utilize JavaScript to build custom frontend components, integrate web elements, and enhance the overall user experience. - DevOps & Automation: Establish, maintain, and optimize CI/CD pipelines to ensure smooth, automated testing, building, and deployment of the GenAI application.

Document Processing: Handson in parsing complex financial documents, unstructured enterprise documents (PDFs, PPTs, tables, Excel, Word, txt, charts, images etc)

Required Skills

- Programming Languages: Deep expertise in Python; solid working knowledge of JavaScript. - Strong experience with Azure OpenAI, Azure Machine Learning, Cognitive Search, and AI Studio. - Solid understanding of LLMs, embeddings, RAG pipelines, vector search, prompt engineering. - GenAI & LLM Frameworks: Extensive hands-on experience with LangChain and LangGraph/ Semantic Kernel / LlamaIndex.

Proven ability to build beyond basic RAG into Agentic and Multimodal implementations. - Backend & APIs: Strong experience building scalable RESTful APIs with FastAPI. - Cloud & DevOps (Azure): Demonstrated experience deploying enterprise-grade applications on Azure.

Deep understanding of Docker, containerization, and configuring automated CI/CD pipelines. - LLM Evaluation & Observability: Hands-on experience with evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and monitoring/tracing tools (e.g., LangSmith, Langfuse, Arize Phoenix). - Databases & Search: Practical experience with Azure AI Search and leading Vector Databases (e.g., Pinecone, Milvus, Qdrant, or Azure Native). - System Architecture: Proven track record of designing systems for parallelism, large-scale data ingestion, and multi-user environments (SaaS architecture). - Frontend/Prototyping: Experience building responsive interfaces using Streamlit,HTML,CSS,Javascript. - Docker, Kubernetes (for model deployment) - Previous experience building commercial AI products or complex advisory/analytical AI agents from the ground up. - Strong understanding of data security and tenant isolation in cloud environments. - Understanding of responsible AI, data privacy, and application security, Guardrails. - Fine-tune and customize foundation models using domain-specific datasets and techniques.

RESPONSIBILITIES Responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures. - Responsible for building out sophisticated AI agent workflows, establishing a scalable cloud infrastructure, and ensuring the application can handle large- scale document ingestion and concurrent user requests seamlessly. - Design and implement highly complex Retrieval-Augmented Generation (RAG) systems, specifically focusing on Agentic RAG (autonomous decision-making workflows) and Multimodal RAG (handling text, images, and other data types).

Backend Development & Orchestration: Build robust, high-performance APIs and backend services using Python and FastAPI. Orchestrate complex LLM workflows and agents utilizing LangChain and LangGraph. - Search & Data Infrastructure: Develop data ingestion pipelines capable of handling exceptionally large documents.

Configure, optimize, and maintain Vector Databases and Azure AI Search for low-latency, high-accuracy retrieval - Frontend & UI Development: Develop rapid, functional, and interactive user interfaces using Streamlit.

Utilize JavaScript to build custom frontend components, integrate web elements, and enhance the overall user experience. - DevOps & Automation: Establish, maintain, and optimize CI/CD pipelines to ensure smooth, automated testing, building, and deployment of the GenAI application.

Document Processing: Handson in parsing complex financial documents, unstructured enterprise documents (PDFs, PPTs, tables, Excel, Word, txt, charts, images etc)

Required Skills

- Programming Languages: Deep expertise in Python; solid working knowledge of JavaScript. - Strong experience with Azure OpenAI, Azure Machine Learning, Cognitive Search, and AI Studio. - Solid understanding of LLMs, embeddings, RAG pipelines, vector search, prompt engineering. - GenAI & LLM Frameworks: Extensive hands-on experience with LangChain and LangGraph/ Semantic Kernel / LlamaIndex.

Proven ability to build beyond basic RAG into Agentic and Multimodal implementations. - Backend & APIs: Strong experience building scalable RESTful APIs with FastAPI. - Cloud & DevOps (Azure): Demonstrated experience deploying enterprise-grade applications on Azure.

Deep understanding of Docker, containerization, and configuring automated CI/CD pipelines. - LLM Evaluation & Observability: Hands-on experience with evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and monitoring/tracing tools (e.g., LangSmith, Langfuse, Arize Phoenix). - Databases & Search: Practical experience with Azure AI Search and leading Vector Databases (e.g., Pinecone, Milvus, Qdrant, or Azure Native). - System Architecture: Proven track record of designing systems for parallelism, large-scale data ingestion, and multi-user environments (SaaS architecture). - Frontend/Prototyping: Experience building responsive interfaces using Streamlit,HTML,CSS,Javascript. - Docker, Kubernetes (for model deployment) - Previous experience building commercial AI products or complex advisory/analytical AI agents from the ground up. - Strong understanding of data security and tenant isolation in cloud environments. - Understanding of responsible AI, data privacy, and application security, Guardrails. - Fine-tune and customize foundation models using domain-specific datasets and techniques.

QUALIFICATIONS BTech / Mtech / IT