Architected and deployed production-grade ML systems with Python, PyTorch, and FastAPI, processing 5M+ monthly requests with 99.9% uptime across 15+ enterprise clients. Designed scalable agent workflows for biometric verification with tool calling, state management, and multi-step orchestration patterns. Optimized deep learning inference pipelines, reducing API latency by 73% from 450ms to 120ms through model quantization (ONNX), GPU acceleration, parallel processing, and intelligent caching strategies. Built comprehensive observability infrastructure with Prometheus, Grafana, and structured logging to monitor model performance, API latency, error rates, and production system health. Implemented distributed caching with Redis (including vector similarity caching), reducing response times by 40% and database load during peak traffic. Developed evaluation pipelines for model performance regression testing, accuracy monitoring, and automated quality gates for production deployments. Deployed containerized ML applications with Docker and Kubernetes on AWS, ensuring scalability, security, and compliance with enterprise requirements. Implemented guardrails for ML inference including prompt validation, cost controls, rate limiting, and audit logging for security and compliance. Coordinated integration between Python ML services and Java Spring Boot microservices using REST APIs and message queues. Led architecture reviews and mentored engineering teams on production AI best practices, evaluation strategies, and reliable system design. Technologies: Python, PyTorch, ONNX, FastAPI, Docker, Kubernetes, AWS, Redis, Prometheus, Grafana, PostgreSQL.

Developed production facial recognition and liveness detection systems using PyTorch with modern architectures (ArcFace, CosFace). Built scalable FastAPI services with async processing, background tasks, and webhook-based callback patterns. Optimized models for real-time inference using ONNX Runtime and TensorRT, achieving sub-200ms response times. Implemented pub/sub messaging with Kafka and Redis Streams for asynchronous agent workflow coordination. Designed evaluation frameworks for model accuracy, latency, and failure mode analysis in production. Technologies: Python, PyTorch, TensorRT, FastAPI, Docker, AWS, Kafka, Redis.

Led design and deployment of production computer vision system using PyTorch, OpenCV, and Django, processing 12K+ daily images with 94% classification accuracy. Optimized real-time inference from 2.5s to 0.8s per image through CUDA optimization, TensorRT quantization, and GPU profiling. Built ML microservices with FastAPI and Django on AWS ECS and Kubernetes, implementing auto-scaling and load balancing. Designed Neo4j graph database solutions for complex product traceability and supply chain relationship modeling. Implemented comprehensive monitoring with custom metrics, alerts, and dashboards for ML pipeline health and data quality. Deployed caching layers with Redis for model outputs and frequently accessed data, reducing database load by 60%. Created evaluation pipelines for continuous model performance monitoring and automatic retraining triggers. Reduced manual inspection workload by 45%, generating estimated $110K in annual savings. Mentored ML/CV teams on production deployment, evaluation strategies, and MLOps best practices. Technologies: Python, PyTorch, OpenCV, Django, FastAPI, Docker, Kubernetes, AWS, CUDA, TensorRT, Redis, Neo4j, PostgreSQL.

Designed and deployed ML-powered document automation platform using TensorFlow, scikit-learn, and FastAPI, serving 6 financial institutions with 250K+ monthly documents at 92% accuracy. Scaled OCR and NLP infrastructure to process 15K+ documents per day using containerized microservices on Azure with Kafka integration. Owned deployment and monitoring for 8+ ML APIs (Django/FastAPI), achieving 99.5% uptime and supporting systems generating $1.5M in annual revenue. Built structured extraction workflows for financial documents using NLP, regex patterns, and ML classification models. Implemented RAG-like retrieval systems for document similarity search and classification using embeddings and vector similarity. Reduced batch processing time from 6 to 3 hours and cut cloud costs by 30% through architectural improvements. Designed Redis caching for frequently accessed documents and Neo4j for customer relationship networks and fraud detection patterns. Created comprehensive evaluation suites with precision/recall metrics, confusion matrices, and error analysis dashboards. Implemented audit logging, security controls, and compliance measures for financial data processing. Improved transaction processing speed by 45% through distributed caching and query optimization. Technologies: Python, TensorFlow, scikit-learn, FastAPI, Django, Java, Spring Boot, Kafka, Docker, Azure, Redis, Neo4j, Angular.

Developed ML document classification system using TensorFlow and scikit-learn, processing 180K+ monthly documents at 91% accuracy for 4 banking clients. Implemented 12+ Python microservices with ML model integration, Docker, and Kubernetes, reducing processing time by 50%. Built OCR system with TensorFlow and OpenCV for automatic extraction of financial document data, improving accuracy by 18%. Created evaluation pipelines with automated testing, performance benchmarking, and continuous model validation. Designed Neo4j solutions for customer-supplier networks and complex ERP relationships, improving decision-making by 30%. Deployed Redis distributed caching for model outputs and frequently accessed data, improving responsiveness by 55%. Reduced annual operational costs by approximately $100K through automation and efficiency improvements. Technologies: Python, TensorFlow, OpenCV, scikit-learn, Java, Spring Boot, Docker, Kubernetes, Kafka, Redis, Neo4j, PostgreSQL.

Developed facial recognition system with PyTorch and OpenCV for monitoring 5,000+ online exams, reducing academic fraud. Implemented predictive models with scikit-learn and Pandas on 50,000+ student behavior records, reducing dropout rates by 87%. Built REST APIs with Django to integrate ML systems with the LMS platform. Created interactive dashboards with Plotly and Streamlit for ML predictions and academic metrics visualization. Designed evaluation frameworks for model fairness, accuracy across demographics, and performance monitoring. Implemented Redis caching for student session data and Neo4j for behavior pattern analysis. Technologies: Python, PyTorch, scikit-learn, Django, OpenCV, Plotly, Streamlit, Docker, AWS, Redis, Neo4j.

Developed deep learning models with PyTorch and TensorFlow for biometric recognition, focused on periocular systems. Implemented advanced image processing, computer vision techniques, and model evaluation frameworks. Published articles in high-impact international journals and conferences, accumulating 1,377+ citations. Designed experiments, evaluation protocols, and statistical analysis for ML model comparison. Collaborated on international biometric research projects with academic and industry partners. Technologies: Python, PyTorch, TensorFlow, OpenCV, Docker, CUDA, NumPy, scikit-learn.

Developed computer vision application for automatic bird species classification using deep learning and signal processing. Implemented feature extraction pipelines and classification models with Support Vector Machines. Technologies: Python, MATLAB, OpenCV, scikit-learn, librosa.

Developed computer vision algorithms with Python and OpenCV for biometric systems. Built web interfaces with Java and PHP for biometric data visualization and management. Implemented embedded systems with Python on Raspberry Pi for real-time image processing. Technologies: Python, OpenCV, scikit-learn, Java, PHP, Arduino, Raspberry Pi.