I turn complex Generative AI research into production-ready, revenue-generating software. Specializing in RAG pipelines, Multi-Agent Workflows, and Cloud-Native Deployments.
With over 2 years of production experience at companies like DataSphere Solutions and PakLogics, I don't just train models—I build resilient AI ecosystems.
My expertise lies in bridging the gap between experimental AI and scalable software. From designing autonomous agents using LangGraph to engineering context-aware RAG systems that eliminate hallucinations, I focus on delivering high-availability solutions.
Whether it's deploying quantized models on edge devices or architecting cloud-native pipelines on AWS/GCP, I ensure every line of code drives measurable business value.
Production-grade RAG systems, multi-agent workflows, and custom LLM fine-tuning
Distributed AI infrastructure, microservices, and cloud-native deployments
CI/CD for ML models, auto-scaling inference, and zero-downtime deployments
Diagnostic systems, computer vision for medical imaging, FDA-compliant pipelines
Leading the integration of LLMs into legacy SaaS products, implementing Hallucination Guardrails and context-aware memory buffers. Designing rigorous CI/CD pipelines for ML models, ensuring zero-downtime deployments for high-traffic applications.
Architected end-to-end RAG pipelines and deployed autonomous systems on cloud infrastructure. Optimized distributed inference systems and implemented auto-scaling solutions that significantly reduced operational costs while maintaining high availability.
The Problem: Healthcare facility needed automated X-ray analysis system to reduce diagnostic time from 2 hours to real-time, maintaining 95%+ accuracy for FDA compliance.
The Architecture: Implemented CNN-based computer vision pipeline with DICOM integration, preprocessing layer for image enhancement, and multi-stage validation system.
Business Impact: Reduced diagnostic time by 85%, achieved 96% accuracy (exceeding FDA requirements), processed 500+ scans daily, saved $200K annually in labor costs.
The Problem: Fortune 500 client needed to process 1M+ internal documents with <100ms query latency for real-time customer support.
The Architecture: Hybrid search with Pinecone vector DB, custom re-ranking pipeline, LangChain agents for context management, Redis caching layer.
Business Impact: Achieved 40% latency reduction, 98.5% accuracy, reduced hallucination rate by 85%, saved $300K annually in manual support costs.
The Problem: SaaS platform required intelligent automation for complex customer workflows involving 5+ decision points.
The Architecture: LangGraph-based multi-agent system with state management, custom tool integration, fallback mechanisms, and comprehensive logging.
Business Impact: Automated 70% of support tickets, achieved 95% accuracy, reduced resolution time from 4 hours to 15 minutes, saved $500K annually.
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DeepLearning.AI / Stanford University
Issued 2024
DeepLearning.AI
Issued 2024
Google Cloud
Issued 2024
Google Cloud
Issued 2024
Issued 2024
Google Cloud
Issued 2024
Expected Completion: 2026
Focus: Advanced Autonomous Systems & Deep Learning Architectures. Specialized coursework in Multi-Agent Systems, Reinforcement Learning, and Medical AI.
Ready to build production-grade AI systems that drive ROI?
I'm available for consulting on high-impact AI projects. Whether you need to build a RAG system, deploy multi-agent workflows, or optimize your ML infrastructure, let's discuss how I can help.