Vyrill

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. Ph.D. is a plus

Experience Requirements:

• Proven experience as an AI/ML Engineer or similar role, with a strong portfolio of successfully deployed models and projects.

• Experience with natural language processing (NLP) and computer vision.

• Start experience and what it takes to deliver high quality products that scale in a fast paced environment

Other Requirements:

• Proficiency in programming languages such as Python, R, or Java, and experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.

• Solid understanding of data structures, algorithms, and statistical methods.

• Experience with data preprocessing, feature engineering, and model evaluation techniques.

• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and containerization technologies (Docker, Kubernetes).

• Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.

• Excellent communication skills, with the ability to explain complex concepts to both technical and non-technical stakeholders.

• Familiarity with version control systems (Git) and continuous integration/continuous deployment (CI/CD) pipelines.

• Understanding and application of MLOps practices and tools for model monitoring and management.

• Willingness to learn fast and continuously push the boundaries, staying on the cutting edge of what’s possible. AI is evolving fast

Responsibilities:

• Design, develop, and deploy machine learning models and AI solutions to solve complex business problems.

• Collaborate with data scientists, data engineers, and software developers to integrate AI models into production systems.

• Conduct thorough research and experimentation to identify the best approaches and techniques for various AI/ML challenges.

• Preprocess and analyze large datasets to extract meaningful insights and prepare them for modeling.

• Develop and maintain scalable and efficient data pipelines for training and deploying AI models.