EXPERIENCE

EXPERIENCE

SELECTED DEPLOYMENTS ACROSS AI, SYSTEMS, RESEARCH, AND INFRASTRUCTURE.

Selected roles with dates, responsibilities, tools, and clearly labeled internal measurements.

7 ROLES2024 / PRESENT7 AREAS

5 roles · selected view

01

AI SYSTEMS / CONTRACT

MD7

Lead AI Engineer

Built internal AI tools for personalized workflows, alerts, and deadline tracking across eight concurrent automation workstreams while reporting to the CTO.

Python / PyTorch / RAG / Agent orchestration

APR 2026 / PRESENT

REMOTE

8concurrent workstreams
98%positive feedback
02

IMMERSIVE AGENTS / CONTRACT

Meta / Reality Labs

VR & AI Engineer

Built a real-time prototype inside Meta Horizon OS that combined user input, environment signals, and interaction telemetry in a multimodal inference path.

PyTorch / Multimodal AI / Horizon OS / VR

JAN 2026 / APR 2026

AUSTIN, TX

<100 msreported prototype latency
03

GPU / INFERENCE / INTERNSHIP

Intel / XeSS

System Software Engineer Intern

Worked on XeSS 2.x model deployment and super-resolution shaders for game pipelines using C++, PyTorch, OpenVINO, HLSL, and DPC++.

C++ / PyTorch / OpenVINO / HLSL / DPC++

MAY 2025 / AUG 2025

AUSTIN, TX

up to 40%reported frame-rate improvement
35%reported inference-latency reduction
25%reported temporal-artifact reduction
04

CLOUD / INFRASTRUCTURE / INTERNSHIP

Nutanix

AI Systems Engineer Intern

Worked on AI infrastructure integration and reliability under enterprise data and access constraints. Implementation details remain confidential.

Cloud infrastructure / AI systems / Governance

JAN 2026 / PRESENT

REMOTE

Enterprisedeployment context
05

ML RESEARCH / RESEARCH

Texas Instruments

AI Research Assistant

Researched model training, evaluation, and fine-tuning for mathematics and physics tasks.

Python / Model evaluation / Fine-tuning / Datasets

MAY 2026 / PRESENT

DALLAS, TX

Researchtraining and evaluation
How to read this page

Specific work first.

Roles are ordered by relevance rather than chronology. Contract, internship, research, and full-time work retain their actual labels.

Company metrics are marked as internal when the underlying benchmark or sample cannot be published.