Machine Learning Engineer Computer Vision, NLP & Backend

3 - 7 years

0 Lacs

Posted:2 days ago| Platform: Shine logo

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Work Mode

On-site

Job Type

Full Time

Job Description

As a Machine Learning Engineer at our company, you will be utilizing your expertise in Computer Vision, Natural Language Processing (NLP), and Backend Development. Your primary responsibilities will include developing ML/DL models for Computer Vision tasks such as classification, object detection, and segmentation, as well as for NLP tasks like text classification, Named Entity Recognition (NER), and summarization. You will also be implementing research papers and creating production-ready prototypes. To excel in this role, you must have a solid understanding of Machine Learning and Deep Learning concepts. Proficiency in tools and libraries like PyTorch, OpenCV, Pillow, TorchVision, and Transformers is essential. You will be optimizing models using techniques such as quantization, pruning, ONNX export, and TorchScript. Moreover, you will be tasked with building and deploying RESTful APIs using FastAPI, Flask, or Django, and containerizing applications using Docker for deployment on cloud or local servers. Your role will also involve writing clean, efficient, and scalable code for backend and ML pipelines. Strong backend skills using FastAPI, Flask, or Django are required, along with experience in utilizing NLP libraries like Hugging Face Transformers, spaCy, and NLTK. Familiarity with Docker, Git, and Linux environments is crucial for this position, as well as experience with model deployment and optimization tools such as ONNX and TorchScript. While not mandatory, it would be advantageous to have knowledge of Generative AI / Large Language Models (LLMs) and experience with MLOps tools like MLflow, DVC, and Airflow. Additionally, familiarity with cloud platforms such as AWS, GCP, or Azure would be a plus. The ideal candidate should hold a Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. This is a full-time position with an evening shift schedule from Monday to Friday. The work location is in person.,

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