Machine Learning Engineer specializing in Computer Vision and NLP developing AI solutions at Leegality. Focus on document intelligence and content processing for Indian businesses.
Responsibilities
Designing machine learning systems, self-running artificial intelligence (AI) software, and specialized models for Computer Vision and Natural Language Processing applications.
Transforming data science prototypes and applying appropriate deep learning algorithms and tools to text and image/document data.
Solving complex CV and NLP problems with multi-layered data types, such as image/document classification, information extraction, semantic search, and object detection.
Optimizing existing machine learning models, with a focus on high-performance model deployment for CV and NLP tasks.
Developing ML algorithms (including large language models/LLMs and computer vision models) to analyze huge volumes of historical text, image, and document data to make predictions and automate workflows.
Running tests, performing statistical analysis, and interpreting test results for CV/NLP model performance.
Documenting machine learning processes, model architectures, and data pipelines.
Keeping abreast of developments in machine learning, Computer Vision, and Natural Language Processing.
Requirements
2+ years of relevant experience in Machine Learning Engineering, with a strong focus on Computer Vision and/or Natural Language Processing.
Advanced proficiency with Python.
Extensive knowledge of ML frameworks, libraries (e.g., PyTorch, Transformers), data structures, data modeling, and software architecture.
Experience with inference optimization frameworks (e.g., ONNX Runtime, OpenVINO, TensorRT).
In-depth knowledge of mathematics, statistics, deep learning (CNNs, RNNs, Transformers), and algorithms.
Superb analytical and problem-solving abilities, especially for unstructured data challenges.
Great communication and collaboration skills.
Excellent time management and organizational abilities.
Experience with cloud platforms (e.g., AWS) for model deployment and MLOps.
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