10+ years of engineering excellence, now architecting the future of AI. Specializing in RAG, Multi-Agent Orchestration, LLM Fine-tuning, and Production-Grade Solutions that drive measurable business impact.
With over 10 years of professional experience, I specialize in architecting intelligent, production-grade solutions that bridge the gap between advanced Generative AI and measurable business impact. I have spent nearly a decade transforming complex datasets into autonomous systems that solve real-world problems and drive enterprise value at scale.
My current focus is leading the end-to-end design and deployment of Enterprise LLM applications. I leverage the Azure OpenAI ecosystem, LangChain, and Retrieval-Augmented Generation (RAG) to deliver automated decision support and high-fidelity contextual insights.
Beyond model development, I specialize in LLMOps and Governance. I implement robust evaluation frameworks, automated prompt engineering pipelines, and rigorous monitoring systems to ensure model reliability, safety, and cost-efficiency in production environments.
Bridging the gap between cutting-edge research and production-grade AI solutions that drive measurable business impact.
Production-grade AI systems delivering measurable business impact across enterprise scales.
Architected a high-fidelity RAG system for processing 10M+ documents. Implemented semantic chunking and hybrid search to bridge the gap between research and impact.
Accuracy
95%
Volume
10M+
Developed a multi-agent framework for automated enterprise decision support. Leveraged LangGraph to reduce operational latency by 60%.
Efficiency
+60%
Architecture
Multi-Agent
Architected a deep learning forecasting engine using Transformer architectures. Delivered sub-second inference for high-frequency decision support.
Reliability
99.9%
Latency
<200ms
Developed a low-latency fraud detection engine using Isolation Forests and XGBoost. Processes streaming transaction data via Azure Event Hubs.
Recall
98.2%
Latency
<50ms
Built a cross-modal retrieval system utilizing CLIP for semantic querying across technical documentation and architectural diagrams.
Search MRR
0.85
Scale
2M+
Designed an automated evaluation pipeline for LLMs monitoring for PII leakage and hallucinations in real-time production environments.
Safety
99.9%
Savings
40%
Architected a forecasting engine using Temporal Fusion Transformers (TFT) to reduce global retail stock-outs.
MAPE
<5%
Impact
$2M
Developed a RAG system for medical researchers. Implemented semantic chunking and re-ranking for clinical accuracy.
Accuracy
96%
Speed
500p/m
Engineered a LightGBM classification model to prioritize financial recovery. Optimized feature selection using SHAP values for explainability.
ROC-AUC
0.89
Lift
+22%
Implemented a Deep Interest Evolution Network (DIEN) to model complex user behavior sequences. Leveraged Attention mechanisms for high-value conversion prediction.
Precision
91%
Impact
+15%
A decade of engineering evolution—from building data foundations to architecting autonomous intelligence.
Tech Mahindra
Pioneer Credit
Insights
Velrada
Aus1Digital