University College London (UCL) | Master of Engineering in Computer Science
| September '20 – June '24

• Grade: First Class Honours
• Minor in Intelligent Systems
• Integrated Masters - Direct continuation from Bachelor's Degree
• https://studentsunionucl.org/clubs-societies/portuguese-speaking-society:
• Computer Graphics: Ray Tracing, Rasterization and Path Tracing with C++.
• Robotics: Forward and Inverse Kinematics, Path Planning and Robot Dynamics with ROS.

Unify | Founding Engineer (AI/ML & Full Stack)
| April '25 – Present

• AI Assistant Communications: Architected the omni-channel communications infrastructure powering the AI assistant — voice calls, multi-channel messaging (Slack, Microsoft Teams, Discord, WhatsApp), emails, and in-app chat/calls — integrating Twilio, LiveKit, platform bot frameworks and third-party APIs.
• Live Meeting Attendance: Built the assistant's ability to join Google Meet and Microsoft Teams calls as a real participant — participant tracking, two-way screenshare, in-meeting chat and auto-leave — via a Recall.ai bridge.
• Voice Identity & Call Recording: Built speaker diarization with voice-fingerprint labelling, splitting co-located speakers sharing a single microphone through per-utterance embedding clustering, alongside call recording/egress and speech-aligned transcripts with in-app playback.
• AI Desktop & Browser Control: Built a vision-based (VLM) computer-use agent that automates tasks across remote Ubuntu, Windows and macOS desktops, with CLI-installable clients for connecting a user's own machine.
• Organisation Support: Built multi-tenant organisation features on top of personal workspaces — member management and invites, role-based access control with custom roles, ownership transfer, and project/resource sharing.
• Integrations: Designed a reusable OAuth integration framework and connectors with per-assistant connect/disconnect flows.
• LLM Interface: Built the in-browser developer environment — terminal (streaming output, scrollback, tab-completion), code editor with live execution, and a persistent CodeSandbox-backed file system with Google Drive sync.
• Tech Stack: Python, TypeScript, Node.js, React.js, Next.js, FastAPI, PostgreSQL, Docker, Kubernetes, GCP, LiveKit, Playwright, Git

Petronas | Machine Learning Engineer (Data Science)
| September '24 – March '25

• End-to-end model development and deployment, such as data analysis, model training, hyperparameters tuning, model selection and cloud deployment on Azure.
• Worked with linear models, trees, random forest, XG boost and light gradient boosting machine (LGBM).
• Pipeline External Corrosion: ML. Built pipeline external corrosion depth & length prediction models of >90% accuracy.
• Pipeline Freespan Formulation: ML. Built subsea pipeline height & length prediction models of >70% accuracy.
• HIP: Computer Vision. Worked on working person detection through object detection with YOLO, pose estimation and vLLM with GPT4o. Involved prompt engineering for better inference performance.
• Tech Stack: Python, MLFlow, Docker, SQL, Azure, SonarCloud, DevOps, Git

Unify | Machine Learning Engineer (Full Stack)
| June '22 – February '24

• Ivy ML Models: Applied Lead. Led the team to expand ML model collection from scratch to 15+ within 4 months, including computer vision and NLP base models for business showcase.
• Ivy Transpiler: Introduced the native compilation feature for optimising performance of ML models.
• Model Hub: Full stack. Responsible in building client applications and API key authentication.
• Console Application: Cloud. Involved in v0 development and API gateway authorisation.
• Ivy Framework: Software Engineering. Unified PyTorch, Tensorflow, JAX and NumPy functions, making them easily accessible through a single library. All codes are also readily compatible with the native libraries.
• Tech Stack: Python, C++, Nvidia CUDA, AMD RocM, Tailwind CSS, Node.js, React.js, Next.js, TypeScript, Fast API, Flask, Prisma, Alembic, SQL, Docker, GCP, AWS, Git, Transformers, Linux

UCL | Generative AI for Music
| September '23 – April '24

• Review: Paper. Studied existing tech related to image and music generations, majority being text-conditioned.
• Overview: Paper. Developed POCs for Diffusion based Music-conditioned Image Generative AI and Transformer based Image-conditioned Music Generative AI.
• Tech Stack: Python, Pillow, NumPy, SciPy, PyTorch, Transformers, Diffusers, Matplotlib, Git

UCL | Network Analysis of Programming Languages
| November '23 – January '24

• Applied PageRank and HITS for analysing key driver in the evolution network.
• Analysed the community structure of the network with modularity.
• Tech Stack: Python, NetworkX, NumPy, Matplotlib, Git

UCL | Multi-Agent Reinforcement Learning in Portfolio Management
| January '23 – April '23

• Explored the effect of different RL algorithms in competitive vs. cooperative MARL settings.
• Algorithms: Advantaged Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO)
• Tech Stack: Python, OpenAI Gym, Stable Baselines 3, Pandas, Git