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Applied Machine Learning / Computer Vision Engineer

🇮🇳 India

CUDA

Python

Docker

Git

GitHub

Machine Learning

Design

Large Language Models

Video

Applied Machine Learning / Computer Vision Engineer

from 🇮🇳 India

We are currently seeking anApplied 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 combiningComputer 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 inPython.

  • Hands-on experience withPyTorch.

  • Practical knowledge ofOpenCV 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 withVision-Language Models.

  • Familiarity with theHugging Face ecosystem.

  • Experience with model inference, evaluation, and optimization.

  • Understanding of how to move models from experimentation into production.

  • Experience usingDocker andGit.

  • 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.)

by @maxrusakovic