Innovative and results-driven AI Engineer with 11 years of experience in designing, developing, and deploying scalable AI solutions. Skilled in leveraging Generative AI, Agentic AI, and data-driven strategies to enhance business productivity, optimize decision-making, and automate complex workflows.
As a Lead AI Engineer, I lead AI Solution Architecture and the design and deployment of enterprise-scale Generative AI systems and Enterprise AI Platforms, leveraging LLMs, RAG-based architectures, workflow orchestration, and AI agent engineering to drive intelligent decision-making and enterprise productivity, while aligning with Responsible AI principles.
I specialize in AI Backend Architecture & Design, engineering scalable, API-driven backend systems as part of Enterprise AI Platforms, integrating LLMs, FastAPI, GraphQL, and microservices to support real-time AI workflows, data processing, and model inference at scale.
With expertise in MLOps, Model Governance & Deployment, I design and implement end-to-end MLOps pipelines using MLflow, Docker, Jenkins, Kubernetes, and AWS SageMaker for automated CI/CD, model deployment, monitoring, and lifecycle management with FastAPI and Elasticsearch.
I've delivered cross-industry AI solutions across finance, insurance, healthcare, and e-commerce, applying Responsible AI and Model Governance to drive measurable and compliant business impact through Generative AI–powered automation tools and intelligent workflow systems.
Design and deployment of enterprise-scale AI platforms with scalable infrastructure and operations
Building intelligent workflow systems and automation tools powered by Generative AI
Leveraging LLMs, RAG architectures, and intelligent agents for enterprise solutions
Architecting multi-agent systems with workflow orchestration and intelligent coordination
Expert in Python development with focus on AI packages and production-ready libraries
Deploying and managing AI solutions on AWS, Azure, and cloud-native platforms
Optimizing large language models through fine-tuning and reinforcement learning techniques
Building advanced RAG pipelines with vector databases and context-aware retrieval systems
The first and only Python package providing universal capability discovery and negotiation across all major agent frameworks - CrewAI, AutoGen, LangGraph, A2A, and custom agents. Revolutionary tool for multi-agent system interoperability.
A simple, Python-first workflow library designed for learning workflow concepts, prototyping, and lightweight task orchestration. Perfect for understanding distributed workflow patterns and building proof-of-concepts.
A lightweight, production-ready library for tracking and optimizing LLM costs. Provides detailed token usage analytics, cost estimation, and budget management for Large Language Model applications.
Open-source Automatic Speech Recognition (ASR) package for speech recognition model training. Built with Keras and TensorFlow, implementing state-of-the-art deep learning techniques for accurate audio transcription.
Production-ready Python package for intelligent task routing in agentic AI systems. Fine-tunes Google's FunctionGemma for high-accuracy, low-latency function routing—99% cheaper and 10-100x faster than GPT-4, with built-in caching and FastAPI server.
Python package for extracting text from images and PDFs using Tesseract OCR. Streamlines document processing workflows with reliable text extraction and multi-format support.
View Project →Great Lakes Institute of Management
Hyderabad, India
Vivekananda Institute of Technology and Science
Karimnagar, India
International Institute of Information Technology (IIIT-H)
I'm always interested in hearing about new projects and opportunities. Whether you have a question or just want to say hi, feel free to reach out!