Machine Learning Engineer

November 11

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💰 Seed Round on 2022-08

Description

• Design, develop, and implement machine learning and deep learning algorithms and models • Explore and define business problems, design and develop AI agent solution for specific tasks • Collaborate with product team and engineers to transform raw data into actionable insights • Design and develop AI Agentic application for automation tasks, familiar with tools and frameworks such as LangChain and Dify • Train, test, and optimize models to ensure accuracy, efficiency, and scalability • Deploy machine learning models into production environments, ensuring reliability and robustness • Monitor and maintain machine learning pipelines, identifying areas for improvement or enhancement • Conduct data preprocessing and feature engineering to prepare data for modeling • Research and evaluate new algorithms, tools, and techniques to enhance model performance • Collaborate with cross-functional teams to integrate AI solutions into business processes and applications • Document processes, models, and experiments for knowledge sharing and reproducibility • Troubleshoot and debug issues related to machine learning models and systems

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field • Experience with AI Agent solution development and framework • Proven experience as a Machine Learning Engineer or similar role • Strong programming skills in Python, R, or similar languages • Familiar with fine tuning techniques on existing models • Experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, Scikit-learn, etc. • Familiarity with cloud platforms (AWS, Google Cloud, Azure) for deploying machine learning models • Strong understanding of data structures, algorithms, and mathematical concepts used in machine learning • Familiar with deep learning techniques and development process • Excellent problem-solving and analytical skills • Strong communication and teamwork abilities • Nice to Have: Experience with MLOps tools for version control, CI/CD pipelines, and monitoring • Knowledge of AI ethics and responsible AI practices • Understanding of model interpretability and explainability • Familiarity with Docker and Kubernetes for containerization and orchestration • Experience with Azure AI Studio or other cloud platform

Benefits

• Employee Stock Option Plan • Great work environment and team culture • Benefits including Telemedicine • Work from home and remote work opportunities • Vacation and paid time off • Casual dress code; wear what’s comfortable