Mukund Pandey — Lead ML Engineer

I design and deploy machine learning systems that automate identity, access, and security decisions at massive scale — focusing on risk modeling, friction reduction, continuous access intelligence, and explainable AI for IAM. My work sits at the intersection of ML architecture, security engineering, and large-scale distributed systems, including: 🔹 Access Modeling & Role Intelligence Building clustering, anomaly detection, and risk/fraction scoring models to continuously evaluate workforce and service-identity access at Meta scale. 🔹 AI-Driven Decision Systems Designing unified scoring engines, shared feature schemas, explainability layers, and continuous pipelines that power risk scoring, access recommendations, and intelligent approvals. 🔹 Agentic Automation for Security Developing autonomous agents that monitor top-line access metrics, detect regressions, correlate root-causes, and recommend or trigger remediation actions across thousands of assets. 🔹 Simulation & Impact Prediction Architecting systems that simulate the downstream effect of an access change (manual or automated), predicting risk deltas, friction spikes, and safety regressions before rollout. 🔹 ML Strategy & Platform Design Driving the convergence of multiple access models (risk-focused, friction-focused, internal tools, TAO) into a shared ML platform using common infra, explainability, and objective tuning. Before Meta, I built real-time ML pipelines and access-risk systems as Director/VP ML Engineer in global banks (UBS, Citi), including entitlement intelligence, anomaly detection, explainability frameworks, and ML infra serving millions of inferences per day. I care about AI for security, agentic automation, IAM transformation, and building ML systems that actually ship, scale, and deliver measurable business value.

Stackforce AI infers this person is a Cybersecurity and AI expert specializing in machine learning for identity and access management.

Location: London, England, United Kingdom

Experience: 17 yrs 6 mos