As a Software Engineer in the Security organization at Salesforce, I develop and deploy machine learning systems for real-time threat detection. I specialize in designing distributed systems that support real-time model training and inference at scale. My work includes building ML pipelines for detecting anomalous and malicious behavior, deploying models into production environments, and developing services that enable enterprise-scale data governance, including automated data quality validation and data lineage tracking across large datasets.
Posts
Software Engineer on the Security team at Salesforce, building and operating ML-powered solutions used for Salesforce-wide security.
Currently building a real-time ML platform for near-real-time threat detection (<60 seconds), supporting production use cases such as login anomaly and data exfiltration detection.
Have worked across the ML platform lifecycle, including data quality, data lineage, pipeline reliability, and data availability for production ML systems.
Strong experience with distributed systems, scalable data/ML pipelines, and platform engineering, with a focus on building reliable and production-ready systems.
Experienced in working on large-scale engineering problems and collaborating across teams to take systems from development to production.
Experience
Software Engineer (MTS) at Salesforce Jun 2024 to now - Bangalore Automated end-to-end remediation of stolen credentials and leaked API secrets across Salesforce Core and Marketing Cloud by building a fault-tolerant service that consumes threat detections from Kafka and orchestrates credential verification followed by password reset, credential suspension, or IP blocking through authenticated cross-cloud APIs. Transformed a batch-based ML threat-detection system into a real-time streaming platform, reducing detection latency by 96% (30 min → <60 sec) and event loss by 90%, by re-architecting centralized Spark batch jobs into a distributed, fault-tolerant pipeline—building the per-region log-consumer and Kafka-based egress service
SWE Intern at Salesforce May 2023 to Jul 2023 - Bangalore Developed a proof of concept for integrating Feast as a feature store for streaming detection jobs, enabling low-latency storage and retrieval of stateful features for real-time model inference.
Education
Indian Institute of Technology (IIT), Hyderabad B.Tech, Artificial Intelligence 2020 to 2024 Relevant Coursework: Data Structures and Algorithms, DBMS, Discrete Maths, Operating Systems, Computer Networks, Foundations of ML, Deep Learning, Image and Video Processing, NLP, Reinforcement Learning, Explainable AI