Gurugram, Haryana, India
Machine Learning and Generative AI Engineer at Wipro, working on enterprise-scale Generative AI, MLOps, and AI infrastructure automation initiatives for ABB (Asea Brown Boveri) as part of the Flex_GenAI_Engg program. Currently contributing as an Infra Automation Engineer – AI (L2) and Developer, supporting production-ready AI systems in a global client environment. Experienced in designing, deploying, automating, and operationalizing AI/ML solutions, with strong exposure to model lifecycle management, pipeline orchestration, CI/CD integration, infrastructure automation, monitoring, and optimization for scalable and reliable AI delivery. Possess a strong technical foundation in Machine Learning, Deep Learning, Multimodal AI, and Agentic AI concepts, with hands-on experience across CNNs, LSTMs, Random Forests, and multimodal architectures integrating vision and speech data. Academic and project work includes multimodal emotion recognition systems, feature extraction and fusion, and exploratory implementation of Quantum Machine Learning (QML) using Quantum Neural Networks (QNNs). Background includes AI-driven systems, cloud platforms, edge computing, and secure infrastructure, enabling the development of robust, automated, and enterprise-grade AI solutions. Actively focused on advancing expertise in Generative AI, agent-based systems, multimodal intelligence, MLOps, and AI platform engineering, with the goal of building high-impact, real-world AI products at scale.
• Developed AI-driven services for smart city infrastructure, enhancing real-time data processing with efficiency by 30%. • Integrated Python script and executed edge computing solutions and achieving a 40% reduction in latency for traffic. • Integrated AWS cloud platform to enhance cybersecurity in smart city networks, decreasing security incidents by 25%. • Deployed AI models on edge devices to optimize smart traffic control and monitoring systems, and investigated methods to visualize and distribute daily test result reports to team members using AWS. Conceptualized and deployed advanced AI-powered services for smart city infrastructure to enhance the efficiency in real-time data processing by up to 30%. Merged Python scripts and executed edge computing solutions that reduced latency within traffic systems by 40%. Improved cybersecurity for smart city networks by integrating the AWS cloud platform, which reduced security incidents by 25%. Deployed various AI models on edge devices for smart traffic control and monitoring systems. Research into the visualization of daily test result reports, which are then distributed to team members using AWS.
• Examined server log files and deployed sophisticated network firewalls, significantly lowering security risks by 35%. • Developed and executed an extensive cybersecurity strategy for managing risks in a smart city traffic control system. • Executed comprehensive security audits, identifying and mitigating 20+ critical high-level vulnerabilities effectively. The course begins with focus on advanced log analysis and network security, whereby the participants learn to investigate server log files under a magnifying glass for suspicious activities, patterns, or anomalies. During this course, the attendee will deep dive into log file structure and make use of some advanced filtering to take their threat detection capabilities to the next level. Hands-on experience in configuring and managing NGFWs will include creating granular-level firewall rules, intrusion prevention systems, and making use of advanced threat intelligence for up to 35% reduction in security risks. This course will also cover the development and implementation of a holistic cybersecurity strategy for infrastructure in smart cities. Students will be exposed to the unique security challenges related to IoT devices and interdependent systems used in traffic control systems within smart cities. In this course, students will learn risk analyses, modeling threats, and developing detailed incident response plans to establish a strong guard against possible cyber threats. Practical exercises will involve the elaboration and application of a multi-tier cybersecurity strategy that would grant the necessary security to complex smart systems. At the end of the course, students will receive a certificate in Advanced Cybersecurity Strategies and Implementation, validating that they can plan and execute sophisticated security challenges and create complex strategies involving traditional and emerging technologies.
• Designed and deployed CPS and IIoT solutions, driving a 20% significant overall increase in manufacturing efficiency. • Designed and implemented robust security protocols for interconnected industrial devices, enhancing system integrity and reliability by 40% with advanced threat detection capabilities always. • Developed real-time monitoring and control systems utilizing data analytics and machine learning technologies effectively. Designed and deployed CPS and IIoT solutions, aiming at achieving a 20% increase in the overall manufacturing efficiency. Implement robust security protocols for interconnected industrial devices, enhancing system integrity and reliability by 40%, with extended advanced threat detection capabilities. Designed real-time monitoring and control systems using data analytics and machine learning technologies to perform effective operational oversight and optimization.