Shiva – AI agent development, Python, LLM

This project was an enterprise AI workflow automation platform built for company's internal business, advisory, and research teams. The product was designed to automate complex knowledge work by using multiple AI agents that could collaborate on tasks such as document analysis, summarization, report generation, and decision support.

The platform helped internal users reduce manual effort in processing large volumes of business and research content. Instead of relying on disconnected tools or human-only review cycles, the system enabled structured multi-step AI workflows where different agents handled specific responsibilities such as extraction, summarization and validation.

The main users were internal advisory teams, research teams, and business users who needed faster access to synthesized information and decision-ready outputs.

Its main features included:

• Multi-agent orchestration using CrewAI and LangGraph.
• Automated document analysis and summarization.
• Decision-support workflow automation.
• Secure API-based integration into internal applications and portals.
• Scalable asynchronous processing with Azure Service Bus.
• Front-end integration for user interaction and output review.

This project highlights Shiva's experience in designing production-oriented agentic AI systems that combine orchestration, scalability, and enterprise usability.

• Designed and deployed AI Agent systems using CrewAI + LangGraph for automated workflow orchestration, enabling multi-agent collaboration for document analysis, reporting, and decision support.
• Developed and deployed AI agent-based systems using Azure Functions, LangGraph, and Azure Service Bus for workflow automation and cross-agent collaboration.
• Integrated the solution into internal web portals using secure REST APIs, serving over 1,500+ users across advisory and research teams.
• Integrated TypeScript-based front-end components with Azure Functions and Flask/FastAPI backends, enabling seamless user interaction with AI models.

This project was an enterprise knowledge intelligence and question-answering platform built to help company's teams search, retrieve, and interact with internal knowledge more effectively. The product used retrieval-augmented generation to provide grounded, context-aware responses instead of relying on generic LLM outputs.

The solution enabled users to ask natural-language questions and receive relevant answers backed by enterprise content stored across internal knowledge sources. It improved knowledge accessibility, reduced time spent on manual searches, and supported faster research and internal decision-making.

The primary users were internal business teams, advisory groups, and research users who needed quick access to trusted organizational knowledge.

Its main features included:

• Enterprise semantic search and contextual retrieval.
• RAG-powered question answering.
• Vector search using Pinecone and FAISS.
• Azure OpenAI integration for grounded response generation.
• Hallucination control and retrieval validation layers.
• Support for chatbot and knowledge assistant use cases.

This project demonstrates Shiva's strong capability in building production-grade generative AI and RAG systems for enterprise knowledge retrieval and decision support.

• Developed retrieval-augmented generation (RAG) pipelines with LangChain + vector databases (Pinecone/FAISS) for enterprise knowledge management and chatbots.
• Implemented LLM-powered Q&A assistants for internal teams using Azure OpenAI GPT models combined with LangChain and Python Azure AI SDK for orchestration.
• Implemented hallucination detection & grounding strategies by integrating retrieval validation layers into RAG pipelines.
• Applied LLM fine-tuning on domain-specific datasets to improve accuracy and reduce bias in responses.

This project was an ML platform modernization and predictive analytics enablement initiative focused on making machine learning delivery more repeatable, scalable, and business-consumable. The product combined predictive modeling, experiment tracking, deployment automation, and dashboard integration.

It helped the company move toward more mature ML operations by improving how models were trained, versioned, deployed, and monitored. It also enabled business teams to consume AI outputs within existing reporting platforms.

The main users were data science teams, analytics teams, and business users consuming predictive insights.

Its main features included:

• Predictive analytics using Spark MLlib.
• Experiment tracking and model versioning with MLflow.
• CI/CD and automated retraining pipelines.
• Deployment with Docker and Kubernetes.
• Integration of ML outputs into Tableau and MicroStrategy.
• Deep learning support for text and image analytics.
• Bias and fairness monitoring within ML workflows.

This project highlights Shiva's strong capability in MLOps, predictive analytics, responsible AI, and enterprise-scale model operationalization.

• Implemented Spark MLlib models (classification, regression, clustering) to support BI and predictive use cases.
• Implemented MLflow for experiment tracking, model versioning, and lifecycle management.
• Established MLOps pipelines (CI/CD + automated retraining) using Azure DevOps + Docker/Kubernetes, ensuring seamless deployment of ML models to production.
• Developed and deployed AI-driven dashboards by integrating ML model outputs into MicroStrategy/Tableau reports.
• Applied neural networks and deep learning techniques (CNNs, RNNs, Transformers) for unstructured data use cases such as text and image analytics.
• Researched and applied methods to mitigate model bias, monitoring weight distributions and fairness metrics across ML pipelines.

This project was a contextual retrieval and knowledge access solution developed for financial data and metadata use cases within the company. The product used a retrieval-augmented generation approach to help internal users find relevant information more accurately than legacy keyword-based search systems.

It improved enterprise search by combining semantic retrieval with LLM-based response generation, enabling users to get better answers for financial and metadata-related questions. The solution was especially useful where traditional search produced low-quality or incomplete results.

The product was built for internal business users, analysts, and teams working with financial data and metadata repositories.

Its main features included:

• RAG-based contextual retrieval.
• Azure AI Search and Cosmos DB integration.
• Reusable retrieval pipelines and prompt orchestration.
• Improved answer relevance over keyword search.
• Support for enterprise financial search and lookup scenarios.
• Higher retrieval accuracy and better user experience.

This project reflects Shiva's practical experience in delivering measurable improvements from GenAI and retrieval technologies in an enterprise banking setting.

• Orchestrated RAG pipelines using Azure AI Search + Cosmos DB for contextual retrieval of financial data, achieving 90% query accuracy improvement compared to legacy keyword systems.
• Developed reusable AI components and utilities for metadata automation, improving reusability across multiple data-integration use cases.