Venkata Rami Reddy Kallu
Generative AI Systems • Safety & Governance • Privacy
Apex, North Carolina

Venkata Rami Reddy Kallu

Senior AI Engineer | Generative AI Systems, Safety & Privacy

I design and build production-grade generative AI systems with enforceable safety, privacy, and governance guarantees — spanning LLM tool execution, runtime validation, and adversarial robustness across text and audio.

What I do

My work sits at the boundary between engineering and applied research, where production constraints require runtime enforcement rather than best-effort heuristics.

Areas I work on:

  • Policy-gated tool execution for LLM agents
  • Deterministic validation of tool inputs and outputs
  • Evidence-backed summarization (anti-hallucination by construction)
  • Privacy-preserving detection of synthetic voice

I’m especially interested in systems that are auditable, reproducible, and resistant to misuse even when models behave unexpectedly or adversarially.

Now

What I’m actively shipping and writing:

  • PolicyGraph: a reproducible safety runtime for MCP tool execution
  • OWASP-aligned evaluation + contract tests for agent safety
  • Follow-up work on runtime enforcement patterns (Paper #2 direction)

Focus

GenAI Systems AI Safety AI Governance Tool-Using Agents Privacy-Preserving ML Synthetic Voice Detection

Featured work

Selected highlights (not a full resume)

PolicyGraph

A policy-gated runtime for safe and auditable Model Context Protocol (MCP) tool execution.

  • Default-deny tool authorization (operator allowlist)
  • Schema-constrained planning (strict JSON only)
  • Typed output validation
  • Evidence-locked summaries (grounded by verbatim quotes)

Patent highlight

Privacy + enforceability theme

Privacy-Preserving Synthetic Voice Detection (Filed systems patent)

I’m a co-inventor on a filed systems patent focused on privacy-preserving synthetic voice detection — detecting AI-generated/spoofed speech while minimizing speaker identity leakage.

  • Detect AI-generated or spoofed speech reliably
  • Preserve speaker anonymity and sensitive identity attributes
  • Support deployment in privacy-sensitive and regulated settings