Job Description

Job Requirements

Job Title and Summary

We are seeking a talented and passionate Machine Learning Engineer to join our innovative development team. You will be instrumental in designing, developing, and deploying cutting-edge machine learning models that power the core functionalities of our evolving application. This role requires a strong understanding of ML principles, practical experience in building scalable ML solutions, and a collaborative spirit to work alongside software engineers and data scientists.

2. Essential Responsibilities

Design, develop, train, and evaluate machine learning models (e.g., classification, regression, clustering, NLP, computer vision) to address specific application requirements. Implement robust MLOps practices, including model versioning, deployment automation, monitoring, and retraining pipelines. Collaborate with data engineers to ensure data quality, availability, and optimal feature engineering for ML models. Integrate deployed ML models seamlessly into the existing application architecture, ensuring high performance and reliability. Conduct experimentation, A/B testing, and performance tuning to continuously improve model accuracy and efficiency. Write clean, well-documented, and testable code in Python or other relevant programming languages. Research and stay abreast of the latest advancements in machine learning, deep learning, and artificial intelligence. Troubleshoot and resolve issues related to model performance, data pipelines, and deployment. Participate in code reviews, technical discussions, and contribute to the overall architectural design of ML systems.

Work Experience

. Required Skills and Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field. Proven 2+ years of professional experience as a Machine Learning Engineer or in a similar role. Strong proficiency in Python and relevant ML libraries (e.g., scikit-learn, pandas, NumPy). Experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch, Keras). Solid understanding of machine learning algorithms, statistical modeling, and data structures. Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and their ML services. Experience with version control systems (e.g., Git). Ability to translate business problems into technical ML solutions. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills.

4. Preferred Skills and Qualifications

Familiarity with containerization technologies (e.g., Docker, Kubernetes). Experience with big data technologies (e.g., Spark, Hadoop) and data warehousing. Knowledge of software development best practices (e.g., CI/CD, unit testing). * Publications or contributions to open-source ML projects.