

AI/ML & GenAI Architect with 15+ years of experience delivering production-grade AI and data platforms across banking, telecom, energy, and enterprise IT. Proven track record of designing end-to-end AI/ML and GenAI systems—from data foundations and feature engineering to MLOps/LLMOps, governance, and secure deployment—in regulated, large-scale environments. Combines strong scientific foundations with hands-on execution to drive measurable business outcomes, including improved decision-making, risk reduction, and accelerated AI adoption across AWS, Azure, GCP, and hybrid on-prem architectures.
Key Initiatives
Flagship Projects
AI & Machine Learning: Machine Learning (Regression, Classification, Clustering), Explainable AI, Time-Series Analysis
Generative AI : LLMs, Retrieval-Augmented Generation (RAG), LLM-based Agents, Prompt Engineering
Data Science : Statistical Modeling, Feature Engineering, Model Evaluation
MLOps / LLMOps : MLflow, Model Deployment & Monitoring, CI/CD for ML
Programming & Frameworks : Python, SQL, scikit-learn, PyTorch, TensorFlow
Data & Platforms : Spark, Kafka, Data Lakes, Feature Pipelines
Cloud AI Platforms : AWS (SageMaker, Bedrock), Azure (Azure ML, Azure OpenAI), GCP (Vertex AI)
Architecture & Governance : Enterprise AI Architecture, Security, Model Governance
Domains : Telecom, Financial Services, Energy Trading
AWS Certified Machine Learning - Specialty
AWS Certified Solutions Architect - Professional
AWS Certified Solutions Architect - Associate
GCP Professional Data Engineer Certification
Professional Cloud Networking Engineer Certification
Professional Cloud Security Engineer Certification
Azure Fundamentals AZ900
Azure Admin Associate AZ104
1. WCDMA Mean User Throughput Prediction Using Linear Regression Algorithm
o IEEE 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN),
o Proposed and validated a linear regression–based model to predict mean user throughput in WCDMA networks, demonstrating how explainable statistical learning can be used to model radio performance KPIs in operational telecom environments.
2. LTE QoS Parameters Prediction Using Multivariate Linear Regression Algorithm
o IEEE 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN),
o Introduced a multivariate linear regression approach for predicting LTE QoS parameters, highlighting the effectiveness of classical ML models for performance prediction, KPI correlation analysis, and network optimization.