JOB OVERVIEW

Responsibilities:
• Design, develop, and deploy machine learning models and algorithms to solve business challenges.
• Work closely with data scientists, software engineers, and business stakeholders to define project requirements and objectives.
• Preprocess, clean, and analyze large datasets to prepare them for model training.
• Optimize machine learning models for scalability, accuracy, and performance in production environments.
• Build and maintain the infrastructure for data pipelines and model training at scale.
• Evaluate and select appropriate machine learning techniques, tools, and frameworks.
• Perform A/B testing, model evaluation, and hyperparameter tuning to ensure robust outcomes.
• Collaborate with cross-functional teams to integrate machine learning solutions into existing systems.
• Stay up-to-date with the latest advancements in artificial intelligence and machine learning.
• Document processes, model architectures, and best practices for team knowledge sharing.

Requirements:
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field (Ph.D. preferred).
• Proven experience in developing and deploying machine learning models in a production environment.
• Proficiency in programming languages such as Python, R, Java, or C++.
• Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization (e.g., Docker, Kubernetes).
• Knowledge of data engineering concepts, including ETL pipelines and databases (SQL/NoSQL).
• Solid understanding of statistical analysis, probability, and mathematical optimization techniques.
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines.
• Excellent problem-solving, analytical, and communication skills.
• Ability to work independently and collaboratively in a fast-paced environment.
Preferred Skills:
• Experience with Natural Language Processing (NLP), computer vision, or deep learning.
• Knowledge of big data technologies like Apache Spark, Hadoop, or similar.
• Understanding of MLOps practices and model lifecycle management.
• Familiarity with edge computing and deploying models on embedded devices.

Starting : $2500