Senior AI/ML Engineer at Iddo Software

Built an AI-driven computer vision SaaS for industrial quality control, automating real-time defect detection and predictive maintenance across manufacturing environments. Implemented a Generative AI platform using LLMs and transformer-based reasoning to interpret telemetry and reduce false alarms by 32%. Designed an agent-based AI maintenance assistant using LangChain, RAG, and custom embeddings for conversational troubleshooting. Implemented streaming and ETL workflows (Kafka, Spark, Airflow) to enrich logs and simulation narratives. Created multi-agent inference pipelines with LLM orchestration and ONNX Runtime, improving latency by 45%. Transitioned batch NLP pipelines to continuous-learning systems enabling real-time adaptation and reinforcement learning loops. Built DL pipelines for multi-class segmentation, anomaly detection, and surface-quality scoring using PyTorch/TensorFlow. Implemented AI monitoring with drift detection, continuous evaluation, and automated retraining cycle

Senior AI/ML Developer at Brainhub

Led AI integration for a digital-twin simulation platform for autonomous mobility and robotics, embedding real-time intelligence and predictive analytics. Designed deep-learning architectures using PyTorch Lightning, Transformers, and sequence modeling for event prediction and command interpretation. Built scalable FastAPI and Flask microservices powering inference models, and orchestrated high-throughput streaming and ETL pipelines with Kafka, Spark, and Airflow to synchronize simulations and telemetry data. Transformed legacy batch pipelines into continuous-learning systems with model monitoring via Prometheus and MLflow. Mentored engineering teams on GPU scheduling, container orchestration, and CI/CD for ML pipelines.

AI Developer at Codewise

Delivered ML-powered fraud detection and campaign-optimization systems for global ad tech networks. Built ML pipelines with scikit-learn and TensorFlow to classify fraudulent traffic, bot behavior, and invalid conversions. Engineered distributed processing pipelines using Kafka and Spark handling tens of millions of events per hour. Implemented data validation, drift detection, and A/B evaluation to align offline training with online performance. Built analytics dashboards with PostgreSQL-based materialized views and stored procedures. Ported experimental ML models from notebooks to production-grade Python packages and conducted internal workshops on model integration and API performance.

Python Backend & ML Engineer at Nortal

Contributed to a scalable sales intelligence and workflow automation platform. Developed backend services and Django REST APIs for lead processing, workflow automation, and CRM synchronization. Implemented asynchronous pipelines with Celery and Redis. Designed and optimized PostgreSQL schemas with indexing and partitioning; integrated third-party CRM systems via REST endpoints with data mapping and secure authentication. Refactored Python modules for maintainability and backend performance. Supported deployment workflows with Docker, Git, and environment-specific configuration practices.