Ahinsak Taksande

Results-driven Sr. Project Engineer with 6.8 years of experience in Python and machine learning. Delivered innovative solutions to enhance operational efficiency and utilized advanced analytics and data visualization tools to support strategic decision-making. Focused on collaborative problem-solving and continuous improvement.

UBS - SOAR Engineering – IBM QRadar Resilient

• Engineered SOAR automation using IBM QRadar Resilient to orchestrate end-to-end incident response workflows.
• Designed and implemented custom playbooks, rules, and response actions for phishing, malware, and access control incidents.
• Integrated QRadar Resilient with SIEM (IBM QRadar), threat intelligence platforms, email security, and EDR tools using REST APIs.
• Enhanced SOC efficiency through automated enrichment, containment, and remediation, improving response consistency and reducing MTTR.

Tech Stack: IBM QRadar Resilient, SOAR, IBM QRadar SIEM, REST APIs, Incident Response, Threat Intelligence, EDR

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HSBC – WPB Reindexing | Intelligent Document Processing

• Built and deployed an ML-driven document classification system on Instabase, applying supervised learning models to convert unstructured financial documents into structured datasets.
• Performed data preprocessing, feature extraction, and model fine-tuning in Python, improving classification accuracy and extraction precision through iterative training and validation.
• Designed scalable automation pipelines (flows, refiners, schedulers, annotation workflows) enabling continuous model evaluation, reindexing, and high-throughput document processing.

Tech Stack: Instabase, Python, Machine Learning, NLP, Data Preprocessing, Model Training & Validation, Automation Pipelines

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Body Language Decoder | Computer Vision & ML

• Developed a computer vision–based behavior analysis system to detect and interpret facial expressions, hand gestures, and body posture from video data.
• Implemented pose estimation and gesture recognition using MediaPipe and OpenCV, with labeled video datasets for training and evaluation.
• Applied machine learning models (scikit-learn) with feature engineering and validation to improve prediction accuracy, robustness, and real-time performance.

Tech Stack: Python, OpenCV, MediaPipe, Scikit-learn, Computer Vision, Pose Estimation, Gesture Recognition

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