We are looking for a highly capable Machine Learning Engineer to optimize and scale our machine learning systems. In this role, you will evaluate existing ML processes, resolve data-related challenges through statistical analysis, and improve the accuracy and performance of our predictive automation tools.

A successful machine learning engineer should have strong data science expertise and practical experience in developing, deploying, and refining ML models. The ideal candidate will consistently drive improvements in predictive automation performance.

Responsibilities

• Collaborate with managers and stakeholders to define, refine, and align machine learning objectives.
• Design, build, and deploy machine learning systems and self-running AI software for predictive automation.
• Convert data science prototypes into functional ML models using the appropriate algorithms, frameworks, and tools.
• Ensure ML algorithms deliver accurate predictions and user recommendations.
• Convert unstructured data into meaningful insights through auto-tagging, image processing, and text-to-speech pipelines.
• Solve complex business problems using multi-layered datasets and optimize existing machine learning libraries, frameworks, and pipelines.
• Develop ML algorithms to analyze large-scale historical datasets and generate reliable forecasts.
• Conduct tests, perform statistical analysis, and interpret experiment results to improve system performance.
• Document all ML workflows, processes, and model architectures.
• Stay updated with the latest advancements in machine learning, AI, and data science.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related field.
• Master’s degree in Computational Linguistics, Data Analytics, or related areas (preferred).
• Minimum 2 years of experience as a Machine Learning Engineer or similar ML-focused role.
• Advanced proficiency in Python, Java, and R for ML development.
• Strong understanding of ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Keras).
• Solid knowledge of data structures, data modeling, and software architecture.
• Deep understanding of mathematics, statistics, algorithms, and optimization techniques.
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration abilities.
• Exceptional time management and organizational skills.