AI / ML Engineer

• Primary responsibility will be to design, develop, and implement machine learning models and algorithms. This involves researching, experimenting, and selecting appropriate models and techniques to solve specific business problems.
• Responsible for preparing and pre-processing large datasets for machine learning tasks. This includes data cleaning, normalization, feature extraction, and transformation to ensure the data is suitable for training and testing machine learning models.
• Will train machine learning models using appropriate algorithms and frameworks. This involves selecting and optimizing hyperparameters, cross-validating the models, and evaluating their performance using various metrics such as accuracy, precision, recall, and F1-score.
• Collaborate with cross-functional teams, including data scientists, software engineers, and stakeholders, to understand business requirements, gather feedback, and iterate on models and solutions. Effective communication and the ability to explain complex concepts to non-technical stakeholders are crucial in this role.
• Contribute to small sections of design review sessions, presenting your work and gathering feedback from the engineering manager or team leader.
• Deals with real-world datasets, understand data quality issues, and apply appropriate methods to prepare data for machine learning tasks.
• Provides feedback to peers during the design and implementation phases while actively seeking guidance from the engineering manager or team leader.
• Contribute to stand-up meetings by identifying potential issues early and proposing preliminary solutions.
• Prepare comprehensive presentations and reports, occasionally presenting them to stakeholders with supervision and guidance from the engineering manager or team leader, ensuring clarity and effectiveness in communication.
• May be required to interpret and report data findings and maintain or update specific business intelligence tools, databases, dashboards, systems, or methods.

Education and Experience Required:

• Bachelor's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline. Master’s degree is desirable.
• Typically, 2-4 years’ experience.

Knowledge and Skills:

• A solid understanding of mathematics, including linear algebra, calculus, and probability theory, is essential for working with machine learning algorithms. Additionally, a good grasp of statistical concepts and methodologies is necessary for model evaluation and analysis.
• Proficiency in programming languages such as Python, R, or Java is expected. Knowledge of relevant libraries and frameworks like TensorFlow, PyTorch, scikit-learn, or Keras is highly beneficial. Experience with SQL for data manipulation and database querying may also be necessary.
• Hands-on experience in developing and implementing machine learning models, including through internships, research projects, or previous job roles where you worked on machine learning initiatives.
• Practical experience with data cleaning, data pre-processing techniques, and feature engineering is important.
• Experience designing and developing machine learning models using algorithms such as linear regression, deciding trees, random forests, support vector machines, or deep learning models is crucial. Familiarity with model evaluation techniques, hyperparameter tuning, and cross-validation is also expected.
• Proficiency in software engineering principles and practices is valuable. Experience with version control systems (e.g., Git), software development methodologies, and deploying machine learning models in production environments is advantageous.
  • Strong communication skills, both technical and non-technical, are important for collaborating with team members, explaining complex concepts, and presenting findings to stakeholders. The ability to work in cross-functional teams and adapt to evolving project requirements is highly valued.