Aaditya Pai

New York, NY

• Funded AI agent security startup. Building next-generation agent security benchmarking and evaluation infrastructure.

New York, NY

• Researching adversarial robustness and security of LLM agent systems: prompt injection, unsafe tool use, and policy enforcement across multi-step workflows.
• Built evaluation infrastructure and attack pipelines across AgentDojo, WebArena, InjecAgent, and ToolBench to measure detector failure modes, attack transferability, and agent vulnerability under adversarial conditions.
• Designed domain-camouflaged injection attacks and provenance-aware defenses for multi-agent systems.

Chicago, IL

• Engineered anomaly detection and validation pipelines over millions of enterprise telemetry records using Python and SQL; generated automated action lists that reduced annual spend by $550K through systematic identification of inactive licenses and unused resources.
• Built scalable ETL automation frameworks with Python, SQL, PowerShell, and REST APIs; defined data quality checks for missing values, anomalous distributions, and schema drift across staging and production workflows.

Singapore

• Deployed CI/CD workflows and service routing infrastructure for distributed ML services; integrated Istio service mesh within Kubeflow across 3+ microservices to improve observability and deployment reliability.
• Engineered real-time monitoring and anomaly detection for a 10+ node Kubernetes cluster on Linux; defined threshold-based alerting rules for latency, request size, and data transfer.

West Lafayette, IN

• Built a CNN with ELU activations for real-time steering prediction in a simulated autonomous driving environment, with a hybrid FAST-ORB feature pipeline and a regression steering model trained on 45K+ frames of sensor data, reaching under 5 degrees mean absolute error at 30 FPS and improving obstacle avoidance accuracy by 30% under real-time latency constraints.
• Won the "Share with the World" VIP Award at the Purdue Undergraduate Research Conference; benchmarked feature extraction methods, evaluated model stability across simulation conditions, and optimized the inference pipeline for real-time deployment.

West Lafayette, IN

• Benchmarked object detection and tracking algorithms under noisy visual conditions across varied lighting and terrain, analyzing accuracy, stability, and failure modes; selected the MOSSE filter through systematic evaluation, reaching 95% tracking accuracy under real-time constraints.
• Built an end-to-end OpenCV perception pipeline integrating detection and tracking for autonomous navigation in the NASA Lunabotics Mining Competition environment, validated across edge cases with documented, reproducible evaluation methodology.