Portrait of Rusiru Erandaka, AI and ML engineer

Rusiru Erandaka

Final-year AI/ML undergraduate with production-focused experience in computer vision, LLM orchestration, RAG pipelines, and edge deployment.

AI & ML EngineerColombo, Sri Lanka+94 710 438 198

8+

Completed AI projects

Portfolio entries backed by CV data

1.5+ years

Production experience

Computer vision and LLM systems

27K+

Dataset samples

Multimodal data curation experience

Experience

Work and Research

Intern AI/ML Research Engineer (Computer Vision)

Arthur C. Clarke Institute for Modern Technologies

Moratuwa, Sri Lanka - Onsite

September 2025 - April 2026

Worked on a Train-Elephant collision prevention system using thermal vision, segmentation, and embedded deployment.

  • Researched a thermal vision-based elephant collision detection system using YOLOv8 and Vision Transformers, reaching 85.66% accuracy in low-visibility conditions.
  • Optimized inference on NVIDIA Jetson TX2 with quantization and memory improvements for strict latency limits.
  • Reduced GPU utilization from 23% to 9% through TensorRT optimization.
  • Built a TCP/IP-based gimbal controller by reverse engineering communication from captured network traffic.
  • Engineered a computer vision pipeline by merging two segmentation models to automate camera rotation and object detection, improving the system's responsiveness.
  • Developed a full-stack satellite image processing web application using Google Earth Engine for flood detection and monitoring in Sri Lanka.

Profile

AI and ML engineering focus

I build practical AI systems with a strong focus on deployment constraints, data pipelines, and model-driven products. My work spans multimodal safety systems, autonomous synthetic data generation, market intelligence pipelines, and agentic business assistants.

Core strengths

  • Agentic AI workflows, computer vision pipelines, and LLM-based systems.
  • Optimization work for constrained environments including NVIDIA Jetson deployment.
  • Dataset engineering, automated publishing, and production-minded ETL flows.

Engineering approach

  • Prefer simple architectures with measurable operational outcomes and low maintenance overhead.
  • Build around repeatability, automation, and model evaluation instead of one-off demos.
  • Focus on fast-moving implementation with clean technical storytelling for each project.

Education

BSc. (Hons) in Electronics & Computer Science

University of Kelaniya, Sri Lanka

August 2022 - Present

CGPA: 3.42 / 4.00

Publication

Multimodal Browser-Based System for Online Child Safety

ICATC 2025 (IEEE)

DOI: 10.1109/ICATC68823.2025.11407778