Machine Learning Engineer

Overview

Key responsibilities

• Collaborate with crossfunctional teams to identify and prioritize business opportunities where machine learning can have a significant impact.
• Design and develop machine learning models algorithms and statistical analysis to address business challenges.
• Implement scalable and efficient processes for data mining preprocessing and feature engineering.
• Conduct exploratory data analysis and utilize visualization tools to communicate findings to stakeholders.
• Build and deploy machine learning solutions in production environments ensuring their reliability and scalability.
• Optimize and finetune existing machine learning models for improved performance and accuracy.
• Research and stay abreast of the latest advancements in machine learning and apply them to enhance existing models and processes.
• Create and maintain documentation for machine learning models including data sources and validation techniques.
• Collaborate with software engineers to integrate machine learning models into new or existing systems.
• Participate in code reviews provide constructive feedback and contribute to the continuous improvement of the machine learning infrastructure.
• Work closely with stakeholders to understand their needs and deliver tailored machine learning solutions.
• Conduct performance evaluation and testing of machine learning models and iterate based on the results.
• Ensure compliance with data privacy and security regulations in all machine learning initiatives.
• Contribute to the development of best practices standards and guidelines for machine learning development within the organization.

Required qualifications

• Bachelors degree in Computer Science Engineering Mathematics Statistics or related fields.
• Proven experience working in a machine learning engineering role developing and deploying endtoend machine learning solutions.
• Strong proficiency in programming languages such as Python R or Java with a focus on data manipulation and analysis.
• Experience with various machine learning frameworks (e.g. TensorFlow PyTorch scikitlearn) and libraries for data manipulation (e.g. Pandas NumPy).
• Solid understanding of statistical analysis hypothesis testing and experimental design.
• Demonstrated experience in building and deploying machine learning models using cloud platforms (e.g. AWS GCP Azure).
• Expertise in deep learning techniques including convolutional neural networks (CNNs) recurrent neural networks (RNNs) and transfer learning.
• Proficiency in natural language processing (NLP) and text mining for unstructured data analysis.
• Strong analytical and problemsolving skills with the ability to translate business requirements into technical solutions.
• Experience in designing and implementing scalable machine learning solutions for realworld applications.
• Excellent communication and collaboration skills with the ability to work effectively in crossfunctional teams.
• Ability to prioritize and manage multiple projects simultaneously in a fastpaced dynamic environment.
• Knowledge of distributed computing and big data technologies (e.g. Hadoop Spark) is a plus.
• Familiarity with agile development methodologies and version control systems (e.g. Git) is preferred.
• Continual learning mindset with a keen interest in exploring new tools and technologies in the machine learning landscape.

machine learning,python,data mining,deep learning,natural language processing,statistical analysis,algorithm development,data visualization