Title: Artificial Intelligence/Machine Learning Engineer

Key Responsibilities:

• Model Development: Design, develop, and optimize machine learning models and algorithms for various applications.
• Data Engineering: Ensure the availability and quality of data required for model training and evaluation.
• System Architecture: Architect and implement scalable machine learning systems and pipelines that integrate seamlessly with existing infrastructure.
• Research and Innovation: Stay up-to-date with the latest advancements in machine learning and artificial intelligence, and apply cutting-edge techniques to solve complex problems.
• Cross-functional Collaboration: Work closely with product managers, software engineers, and domain experts to understand requirements, set project goals, and deliver high-quality solutions.
• Code Quality and Best Practices: Maintain high standards of code quality and documentation. Promote best practices in machine learning and software engineering within the team.
• Performance Monitoring: Develop and implement metrics to monitor and evaluate model performance in production environments, and make iterative improvements as needed.

Qualifications:

• Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. Advanced degrees (Master’s or Ph.D.) are a plus.
• Experience: Minimum of 2 years of experience in machine learning and data science roles, with a strong portfolio of successful projects.
• Technical Skills: Proficiency in programming languages such as Python, R, or Java, and experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Modeling Expertise: Knowledge of machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Data Handling: Experience with data preprocessing, feature engineering, and working with large datasets.
• Deployment: Familiarity with model deployment and scaling, including experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker).
• Analytical Skills: Strong problem-solving skills and the ability to derive actionable insights from complex data.
• Communication: Excellent verbal and written communication skills, with the ability to convey technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Knowledge of big data technologies (e.g., Hadoop, Spark).
• Experience with MLOps and model management tools.