ML Engineer

Job Brief:

• As a Machine Learning Engineer specializing in Computer Vision (CV) and Natural Language Processing (NLP), you will develop solutions to interesting technical problems, exploring exciting growth opportunities and having a real impact on our product, particularly focusing on document and content intelligence. 
• To ensure success, you should demonstrate solid data science knowledge and experience in a related ML, CV, or NLP role. A first-class engineer will be someone whose expertise enhances our systems for document intelligence and content processing
   

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.