Arkaan QuanungaRoku · AI Innovation
Career Trajectory

Engineering Experience & Impact

A chronological breakdown of software engineering roles, technical ownership, and measured systems impact across streaming devices, AI-driven video compression, and automated infrastructure.

Download Official CV (PDF)Published NVIDIA DevBlog
Recognition

Patent pending and a published NVIDIA article

Patent pending · US & EU

Autonomous software verification

The Roku stack — Explorer, QANTUM, and Forge — is a patent-pending invention for turning manual QA into a data-driven process that finds functional and visual defects on devices with less manual oversight.

Read it on the career timeline
NVIDIA Developer Blog

Enabling Customizable GPU-Accelerated Video Transcoding Pipelines

Written with NVIDIA engineers at V-Nova. The article covers VC6 codec performance and a GPU-accelerated transcoding pipeline, published on NVIDIA’s developer blog.

Read the NVIDIA article

Roku

CURRENT

Software Engineer, AI Innovation & Automation

May 2025 – PresentLondon, UK

Designing and shipping autonomous QA platforms, multi-agent AI systems, and automated test-generation pipelines to modernize streaming device quality engineering.

Quantifiable Achievements & Responsibilities:

  • Shipped Roku Explorer as a production autonomous QA platform adopted by multiple engineering teams for exploratory and focused testing on Roku device UIs — combining Reinforcement Learning (RL), RAG with ChromaDB, and Vision-Language LLM reasoning to navigate interfaces and surface functional/visual bugs with minimal manual oversight.
  • Built change-driven exploratory testing: analyzes code diffs landing in main development repositories, automatically generates targeted exploratory and focused testing scenarios for at-risk surfaces, and commands Explorer to execute them on physical devices — closing the loop from code change to automated device exploration.
  • Architected QANTUM, a Slack-native multi-agent QA orchestration platform that routes complex work across specialized tools via an extensible capability protocol — including shared agent memory, user memory, Model Context Protocol (MCP) tooling, and automated MR creation and validation flows.
  • Developed Forge, an AI automation agent that investigates product-area workstreams, creates automation tickets, and authors and executes regression tests with multi-ticket git worktrees and automated review/CI delivery gates.
  • Scaled AI-assisted delivery across these platforms using Docker/EC2 containerization, GitLab CI pipelines, Jira/Slack integrations, and parallel agent execution workflows.
  • This foundational work represents a patent-pending innovation (US/EU) aimed at transforming manual QA into a data-driven, autonomous process that surfaces functional and visual defects faster while reducing engineering overhead.
Core Technologies & Tooling:
PythonPyTorchTensorFlowLiteLLMChromaDBQdrantRL (PPO/LSTM)Claude Agent SDKMCPFastAPIFlaskMySQLDockerGitLab CIJira APISlack Bolt

V-Nova

Software Engineer, AI & Video Technologies

Sep 2022 – May 2025London, UK

Engineered distributed cloud test harnesses, automated video quality metric pipelines, and cross-platform SDK tracking for next-generation video compression codecs (LCEVC & VC6).

Quantifiable Achievements & Responsibilities:

  • Delivered PaPeR (Parallel Performance Runner) to Meta, innovating in video compression testing and improving testing scalability through an automated cloud framework — accelerating the integration of V-Nova technology into Meta services including WhatsApp, Instagram, and XR platforms.
  • Collaborated with NVIDIA on VC6 performance analysis, integrating VC6 for distributed execution across cloud GPU clusters and co-authoring a technical article on the official NVIDIA Developer Blog on GPU-accelerated video transcoding pipelines.
  • Managed client certification tooling from initial concept to production PoC, automating perceptual video quality measurements (VMAF, PSNR) and functional testing at scale.
  • Led the transformation of internal cloud testing infrastructure from monolithic services to a microservices architecture, achieving a 40% enhancement in system scalability and a 50% reduction in maintenance overhead.
  • Developed DRIVE (Devices Routing Integration for Video Encoding) across Android and iPhone testbeds, yielding an 800% increase in mobile video analysis velocity and an 86% reduction in device setup time, later expanding to battery and decoding benchmarks.
  • Enhanced cross-platform SDK performance tracking across Windows, Linux, and macOS by developing an automated KPI telemetry system that improved release reliability.
  • Mentored junior engineers, fostering technical excellence and driving a measured 15% improvement in team engineering productivity.
Core Technologies & Tooling:
PythonC++AWS (EC2, S3, RDS, Lambda)TerraformDockerKubernetesFFmpegFlaskReactMySQLAnsibleJenkinsVideo CodecsAndroid ADB

The Software Institute

Graduate Software Automation Test Engineer

Aug 2021 – Sep 2022London, UK

Delivered technical consulting and automation frameworks for high-profile clients including Qualitest and British Telecom (BT).

Quantifiable Achievements & Responsibilities:

  • Contributed to strategic consulting engagements with Qualitest and British Telecom (BT), achieving a 20% increase in project delivery efficiency.
  • Designed and deployed automated testing frameworks using Java, JavaScript, Selenium, and Cucumber BDD — expanding regression test coverage by 40% and cutting regression testing cycle times in half.
  • Optimized CI/CD pipelines utilizing Jenkins and AWS, reducing build deployment times by 35% and doubling release frequency.
  • Led eSIM architecture integration testing across smartwatches, iOS, and Android ecosystems, maintaining 99.9% system uptime during carrier pilot programs.
  • Supported validation of cutting-edge architectures for Apple and Google smartwatch wearable OS platforms.
Core Technologies & Tooling:
JavaJavaScriptSeleniumCucumber BDDSpring BootJenkinsAWSCI/CDSQLeSIM

Riztech Construction Ltd

Junior Engineer (Internship)

May 2018 – Jun 2020London, UK

Supported senior structural engineers with computational modeling, energy efficiency calculations, and workflow automation.

Quantifiable Achievements & Responsibilities:

  • Engineered a Python-based calculation suite to automate structural cost estimation, significantly improving calculation accuracy and reducing turnaround time.
  • Collaborated with senior engineers on energy savings calculations and environmental compliance validation.
  • Enhanced team operational productivity by 21% through custom task automation scripts and earned special commendation for innovation.
Core Technologies & Tooling:
PythonHyperworks CADData ModelingMathematical OptimizationEnergy Analysis

Looking for deep-dive technical references?

Detailed architecture case studies for Explorer, QANTUM, Forge, and Augma are available in the Systems section.

View Case StudiesEducation & Certifications