AI Machine Learning Engineer

Roles and responsibilities

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

• Deploy machine learning models into production environments, ensuring they are scalable, traceable and maintainable. This may involve using cloud services or on-premise solutions.

• Collaborate with data scientists, data engineers, software developers, and business stakeholders to understand requirements and deliver ML-driven solutions.

• Conduct data preprocessing, feature engineering, model development, model evaluation, and monitoring to ensure high-quality ML solutions.

• Stay up-to-date with the latest trends and technologies in machine learning and implement them as needed.

• Participate in code reviews, testing, and documentation to ensure high-quality deliverables.

• Improve, monitor and maintain deployed ML models to ensure they continue to meet performance, reliability and accuracy requirements.

• Contribute to the development of best practices and standards for machine learning within the organization, by developing CI/CD templates and standardized frameworks and the creation of guidelines and processes for Data Science teams to use MLOps capabilities.

• Develop and implement containerization strategies for machine learning applications using tools such as Docker and Kubernetes.

Education and experience

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.

• 5+ years of experience in machine learning, data science, or a related field.

• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, etc.

• Proficiency in programming languages such as Python.

• Experience with Azure cloud platform is a must; knowledge of GCP is preferred.

• Knowledge of MLOps, data preprocessing, feature engineering, model evaluation, testing, and CICD techniques.

• Experience with deploying and maintaining ML models in production environments.

• Familiarity with industry-related standards and regulations e.g., GDPR, ISO 27000, etc. is a plus.

• Mentor junior team members and continuously improve the AI services and the overall competence.

Skills

• A strong passion for machine learning and its applications in solving real-world problems.

• A customer-centric and value-driven mindset and approach to developing ML solutions.

• Strong analytical and problem-solving skills and the ability to make data-driven decisions.

• Excellent collaboration and communication skills and the ability to work effectively with different stakeholders and teams.

• Strong knowledge of machine learning algorithms, techniques, and best practices.

• Understanding of data architecture and data modeling concepts.

• Familiarity with agile and scrum methodologies and best practices.

• Strong familiarity with software engineering practices and Python as preferred programming language.

• Ability to drive automation for improved efficiency and scalability.