Research and engineering positions

AI Research Engineer

IQAI · Embodied AI & Real-Time LLM Systems

Real-time speech-to-speech AI pipelines, LLM reasoning and robot control for an embodied humanoid AI system, with responsibility for latency and reliability.

• Built real-time speech-to-speech pipelines integrating streaming speech recognition, LLM reasoning and text-to-speech for an embodied humanoid AI system.
• Benchmarked GPT-4o, Gemini, Groq, ElevenLabs and Cartesia on latency and reliability, and optimised voice-activity detection to reduce conversational turn-detection latency.
• Developed a Python robot control framework and action API supporting locomotion, gaze, end-effector control and real-time interpolated joint control across simulation and physical hardware.
• Designed and deployed an event-driven system connecting on-chain events to autonomous robot behaviours, executing 98 action sequences over 10 days without failure.

AI Research Engineer Fellow

InstaDeep · InstaGeo team (AI for Social Good) · Young African AI Research Fellowship

Scientific machine learning for Earth observation: physics-informed retrieval of vegetation biophysical parameters from Sentinel-2 imagery using the PROSAIL radiative transfer model.

• Developed a self-supervised framework integrating a PyTorch implementation of PROSAIL to train a ResNet-MLP for canopy parameter retrieval (leaf area index, canopy chlorophyll content, canopy water content).
• Reduced memory overhead by 60% through chunk-based data generation (batch sizes up to 5k–10k) and optimised GPU usage.
• Implemented parameter bounding with scaled sigmoid transforms to prevent physically invalid reflectance in the radiative transfer simulations.
• Validated model performance against Sentinel-2 in-situ measurements of LAI and CCC; co-developed the physics-informed Transformer-VAE reported in arXiv:2511.10387 with Prince Mensah, Ibrahim Salihu Yusuf and Arnu Pretorius.

AI Researcher, Radiology NLP

AIMS South Africa / University of the Western Cape collaboration

Thesis research on automatic radiology report generation from 3D chest CT, with Mehrdad Ghaziasgar (UWC), published in Springer LNICST (PAAISS 2025) and awarded 2nd Best Paper.

• Designed a multimodal architecture combining a RadImageNet-pretrained ResNet-50 and GPT-2 to generate expert-level radiology reports from 3D chest CT scans.
• Devised a 3D-aware Axial-Coronal-Sagittal (ACS) convolution strategy to adapt 2D CNNs to volumetric CT data, and integrated cross-attention layers into GPT-2 under a single 24 GB GPU budget.
• Achieved BLEU-1 of 0.403 and perplexity of 2.34 on CT-RATE while training on about 20% of the dataset (~11.6k volumes), approaching the state of the art (CT2Rep) under a 3 TB compute budget.

Graduate Assistant

Obafemi Awolowo University · Department of Electronic and Electrical Engineering

Taught, supervised and graded more than 20 undergraduates in assignments and tests for EEE301, Microelectronics Devices and Circuits I, under the supervision of Dr. O. O. Ilori.

MSc in AI for Science

African Institute for Mathematical Sciences (AIMS) South Africa · Affiliated university: University of Cape Town · Google DeepMind Scholar · Overall Distinction

Graduated with Overall Distinction; named Salutatorian and received the Academic Excellence Award at the AIMS recognition of awards ceremony (2024).

• Thesis: “Automatic Radiology Report Generation from 3D CT Scans Using GPT-2 Pre-Trained Large Language Model (LLM)”, in collaboration with the University of the Western Cape; published in Springer LNICST vol. 697 (PAAISS 2025).
• Google DeepMind Scholarship for the AI for Science programme (2023 – 2024).

BSc (Hons) Electronic and Electrical Engineering

Obafemi Awolowo University (OAU), Osun State, Nigeria · Second Class Upper Division · top 5% of class

Final CGPA 4.46/5.0 (89%), major GPA 5.0/5.0. Thesis: “Design and Construction of a Bidirectional Net Energy Meter with Web and SMS Notification”.

• 2nd Overall Best Student in Control Engineering, Electronic and Electrical Engineering (2021).
• Led undergraduate design groups: a strain-gauge weight sensor, an SVM-based anomaly detector for over- and under-voltage in power systems, and an automatic irrigation system.