Pradyoth Prashanth

JPMorganChase

AI/ML Data Platforms Security · Gen AI Red Team

Feb 2026 — Present

Bengaluru, India

Security Engineer II

Building JPMC's Gen AI Red Teaming capability — the adversarial testing layer that stands between experimental LLMs and production deployment across one of the world's largest financial institutions.

• Researched, Designed and Developed a toolkit to conduct Gen AI Red Teaming Exercises for all AI use cases in JPMC.
• Led several Red Teaming Assessments for top AI Initiatives at JPMC uncovering multiple critical and high-severity vulnerabilities across prompt injection, data leakage, system prompt leakage, intent drift, misuse scenarios and more.
• Set up Network Broker to facilitate connection between our AI Red Teaming App and the AI Red Teaming External Vendors Instance.
• Updated the application with middlewares to establish connectivity to the AI Use cases.
• Designed several new ECS Tasks to expand red teaming coverage with multiple tools.
• Designed advanced manual red teaming methodologies for agentic AI systems to uncover behavioral vulnerabilities, privilege misuse, intent misalignment, and multi-step exploitation paths beyond automated evaluations
• Analysed and Evaluated Coding Sandboxes from a security perspective during an internal hackathon

JPMorganChase

AI Controls Engineering · Cybersecurity & Tech Controls

Jul 2023 — Jan 2026

Bengaluru, India

Security Engineer I

Built the AI/ML security control layer that gates every externally sourced model entering JPMC — preventing vulnerable or tampered models from reaching production systems that touch trillions in assets.

• Built the Model Scanning Platform from the ground up: a security gate that enforces procurement and periodic re-evaluation of 2,500+ externally sourced ML models, ensuring no unvetted model reaches JPMC's production AI infrastructure
• Designed a Model Risk Scoring system that transforms raw vulnerability scanner output into a structured, ranked risk signal — enabling security reviewers to triage findings at scale instead of parsing unstructured reports by hand
• Eliminated a manual compliance reporting bottleneck by shipping an event-driven Kafka pipeline that generates real-time, auditable control attestations across every model lifecycle event — directly accelerating regulatory readiness
• Overhauled the model security report format with risk-aligned mapping to internal and regulatory standards, making findings interpretable to non-security stakeholders and cutting review turnaround time during audits
• Cut end-to-end test suite execution time by 67% through automation refactoring — tightening the feedback loop for every release while enforcing secure-coding standards and remediating high-severity scanner findings before they could ship

JPMorganChase

AI Controls Engineering · Cybersecurity & Tech Controls

Jan 2023 — Jun 2023

Bengaluru, India

Software Engineer Intern

Shipped production backend code for the ML scanning platform on day one — treated as a full engineer, not an intern. Recognized with the Super Squad Team Award for delivery quality.

• Built the core backend services and REST APIs that power the model scanning workflow — job orchestration, status tracking, and evidence collection — forming the operational backbone of what became a firm-wide ML security platform
• Brought test coverage from near-zero to 80% by authoring unit and integration test suites for every new feature, catching regressions early and enabling reliable release cadence
• Eliminated idle cloud spend across non-production environments by implementing Lightswitch scheduling, Janitor cleanup policies, and S3 Lifecycle tiering
• Standardized infrastructure-as-code across DEV, UAT, and PROD with Terraform — eliminating configuration drift and ensuring consistent, auditable deployments across all environments

LogicHive Solutions Pvt. Ltd.

Aug 2021 — Feb 2022

Mysuru, India

Engineering Intern

Designed and built a real-time asset tracking system from scratch — hardware to backend — giving enterprise clients live visibility into physical asset location and movement for the first time.

• Engineered the full tracking stack: Raspberry Pi + BLE beacons for device detection, MQTT for low-latency telemetry transport, and Redis for sub-millisecond location state reads — all integrated into a working PoC in 7 months
• Built the complete data pipeline — ingestion, processing, and visualization — enabling operations teams to monitor asset location, movement history, and status in real time, replacing a previously manual inventory process