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.

Requirements

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.)