Rahul Bansal

Technical Success Manager @ Zscaler | Security Automation & Scripting | Cloud & Zero Trust | AI/ML Enthusiast | Ex-MITACS Research Intern

Chandigarh, India

About

I'm a Security-focused Technical Success Manager at Zscaler with 4+ years of experience across cloud security, automation, and customer success. I specialize in solving technical challenges using scripting (Python/Bash) and have a strong foundation in network protocols, Zero Trust architecture, and cloud platforms (AWS, Azure). Alongside my current role, I’m passionate about Artificial Intelligence — having completed 8+ MOOCs (Coursera, IBM, deeplearning.ai) and recently built an ML-based anomaly detection project for network logs. This blend of security, automation, and AI is where I thrive. My career goal is to transition into a Solutions Architect or Security Automation Engineer role — where I can use my client-centric mindset, hands-on scripting, and AI background to design intelligent, scalable systems. 📌 Skills: Python, Zscaler, SASE, Zero Trust, SCIM, SAML, Azure, AWS, Anomaly Detection, AI/ML, Customer Success 📚 Ongoing Projects: AI-Based Log Anomaly Detection (GitHub soon) 🎯 Open to collaborating, learning, and solving interesting problems in the Security + AI space.

Experience

  • Zscaler (4 yrs 5 mos)
    • Technical Success Manager
      Jan 2025 - Present · 1 yr 6 mos

    • Technical Account Manager
      Aug 2024 - Jan 2025 · 6 mos

    • Associate Technical Account Manager
      Nov 2022 - Aug 2024 · 1 yr 10 mos

  • Mitacs Globalink Research Intern at Université de Sherbrooke
    May 2021 - Jul 2021 · 3 mos

    Implemented a Data Science Workflow based on analytics, text mining and AI to support the activity of scientific literature review. - Collection of data using Web Scraping (Scrapy, splash) - Text Mining from the collected data (PyPDF2, PyMuPDF, regex) - Cleaning the data for Machine Learning Model (Data Wrangling). - Applying different Machine learning algorithms for various analyses. (logistic regression, Cosine Similarity)