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.
As an Applied ML / Computer Vision Engineer, you will:
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Design, develop, and improve Computer Vision and image-processing pipelines.
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Build solutions for video analysis, video understanding, and real-time visual processing.
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Develop multimodal AI systems combining vision, audio, and language data.
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Work with object detection, image segmentation, classification, tracking, and related Computer Vision techniques.
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Implement and fine-tune Vision-Language Models and Transformer-based architectures.
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Integrate Large Language Models into AI products for reasoning, analysis, and automated decision-making.
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Evaluate, optimize, and deploy machine-learning models in production environments.
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Improve model inference speed, accuracy, scalability, and resource efficiency.
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Translate research papers and emerging AI techniques into practical product features.
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Build APIs and production-grade services for AI model integration.
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Collaborate with engineering and product teams to understand business requirements and deliver effective AI solutions.
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Monitor model performance and continuously improve deployed systems.
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Maintain clear, reusable, and well-documented code.
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1–4 years of experience in Applied Machine Learning, Computer Vision, Deep Learning, or a related field.
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Strong programming skills in Python.
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Hands-on experience with PyTorch.
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Practical knowledge of OpenCV and image or video processing.
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Strong understanding of Deep Learning fundamentals.
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Experience with object detection and/or image segmentation models.
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Knowledge of Transformer architectures.
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Experience working with Vision-Language Models.
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Familiarity with the Hugging Face ecosystem.
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Experience with model inference, evaluation, and optimization.
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Understanding of how to move models from experimentation into production.
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Experience using Docker and Git.
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Ability to independently investigate technical challenges and propose practical solutions.
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Ability to understand, reproduce, and implement methods described in research papers.
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Strong analytical and problem-solving skills.
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Curiosity and willingness to explore new technologies.
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Passion for AI, Machine Learning, and product development.
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Ownership mindset and accountability for delivered solutions.
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Ability to work independently and contribute within a collaborative team.
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Interest in building practical products rather than focusing only on model experimentation.
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Comfortable working in a fast-moving and research-driven environment.
Experience with one or more of the following technologies would be considered an advantage:
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YOLO
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GroundingDINO
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SAM or SAM2
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Florence-2
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Whisper
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Qwen2.5-VL
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LLaVA
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InternVL
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CUDA
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TensorRT
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ONNX
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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.)
