Glint Tech Solutions
About the Role
We are looking for a Computer Vision / Machine Learning Engineer to join a fast-growing, product-driven team working on real-world AI applications.
This role focuses on building and optimizing lightweight visual models for production environments, with strong emphasis on performance, efficiency, and deployment.
What You’ll Do
- Model Selection
Choose the most suitable approaches (traditional algorithms vs. deep learning) based on real-world use cases and constraints - Performance Optimization
Optimize models by balancing accuracy, latency, memory, and power consumption - Model Training & Compression
Train and optimize lightweight models using techniques such as quantization, pruning, and knowledge distillation - Deployment & Optimization
Deploy models to production environments (mobile/edge), ensuring low latency and high efficiency - End-to-End Ownership
Drive the full lifecycle from data analysis model development optimization deployment - Application Performance
Improve overall application responsiveness and resource usage to ensure smooth user experience
Education
- Bachelor’s degree with 3+ years of experience, OR
- Master’s degree with 1+ year of experience, OR
- PhD in a related field
Technical Skills
- Strong foundation in Computer Vision and Machine Learning
- Experience with model optimization for production (latency, memory, power trade-offs)
- Hands‑on experience with lightweight model deployment (e.g., ONNX, TFLite, CoreML)
- Experience with model compression techniques (quantization, pruning, distillation)
- Solid programming skills (Python + ML frameworks such as PyTorch or TensorFlow)
Experience
- Experience building and deploying models in real-world production environments
- Familiar with edge/mobile AI scenarios or performance‑constrained systems
Soft Skills
- Strong problem‑solving ability and ownership mindset
- Comfortable working in a fast‑paced, high‑growth environment
- Able to independently drive projects end‑to‑end
Nice to Have
- Experience with mobile/edge deployment (iOS / Android)
- Experience optimizing models for real‑time applications
- Background in performance‑critical systems
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