Applied Machine Learning / Computer Vision Engineer

We are currently seeking an Applied Machine Learning / Computer Vision Engineer to join one of our clients’ innovative AI teams.

This is an exciting opportunity to work on a next-generation AI platform combining Computer Vision, Multimodal AI, Applied Machine Learning, and Large Language Models.

Key Responsibilities

As an Applied ML / Computer Vision Engineer, you will:

• Design, develop, and improve Computer Vision and image-processing pipelines.
• Build solutions for video analysis, video understanding, and real-time visual processing.
• Develop multimodal AI systems combining vision, audio, and language data.
• Work with object detection, image segmentation, classification, tracking, and related Computer Vision techniques.
• Implement and fine-tune Vision-Language Models and Transformer-based architectures.
• Integrate Large Language Models into AI products for reasoning, analysis, and automated decision-making.
• Evaluate, optimize, and deploy machine-learning models in production environments.
• Improve model inference speed, accuracy, scalability, and resource efficiency.
• Translate research papers and emerging AI techniques into practical product features.
• Build APIs and production-grade services for AI model integration.
• Collaborate with engineering and product teams to understand business requirements and deliver effective AI solutions.
• Monitor model performance and continuously improve deployed systems.
• Maintain clear, reusable, and well-documented code.
• 1–4 years of experience in Applied Machine Learning, Computer Vision, Deep Learning, or a related field.
• Strong programming skills in Python.
• Hands-on experience with PyTorch.
• Practical knowledge of OpenCV and image or video processing.
• Strong understanding of Deep Learning fundamentals.
• Experience with object detection and/or image segmentation models.
• Knowledge of Transformer architectures.
• Experience working with Vision-Language Models.
• Familiarity with the Hugging Face ecosystem.
• Experience with model inference, evaluation, and optimization.
• Understanding of how to move models from experimentation into production.
• Experience using Docker and Git.
• Ability to independently investigate technical challenges and propose practical solutions.
• Ability to understand, reproduce, and implement methods described in research papers.
• Strong analytical and problem-solving skills.
• Curiosity and willingness to explore new technologies.
• Passion for AI, Machine Learning, and product development.
• Ownership mindset and accountability for delivered solutions.
• Ability to work independently and contribute within a collaborative team.
• Interest in building practical products rather than focusing only on model experimentation.
• Comfortable working in a fast-moving and research-driven environment.

Nice to Have

Experience with one or more of the following technologies would be considered an advantage:

• YOLO
• GroundingDINO
• SAM or SAM2
• Florence-2
• Whisper
• Qwen2.5-VL
• LLaVA
• InternVL
• CUDA
• TensorRT
• ONNX
• FastAPI

(Exceptional fresh graduates may also be considered if they can demonstrate strong technical knowledge through relevant academic work, personal projects, GitHub repositories, Kaggle participation, research, or a technical portfolio.)