Platform Machine Learning Engineer [28714]

Job Summary: As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning models and algorithms to address complex business challenges. You will collaborate with cross-functional teams to integrate these models into production systems, ensuring they deliver actionable insights and drive business value. The ideal candidate has a strong background in machine learning, data science, and software engineering. Key Responsibilities:

• Design and develop machine learning models and algorithms for various business applications.
• Collaborate with data scientists, software engineers, and product managers to define project requirements and deliver scalable solutions.
• Preprocess and analyze large datasets to extract meaningful features and patterns.
• Implement and optimize machine learning pipelines and workflows.
• Deploy and maintain machine learning models in production environments.
• Monitor model performance and perform ongoing evaluation and tuning.
• Stay updated with the latest advancements in machine learning and artificial intelligence.
• Document model development processes and maintain code repositories.

• Bachelor’s or Master’s degree in Computer Science, Engineering.
• 3+ years of experience in machine learning, data science, or a related field.
• Strong proficiency in Python.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, or Keras.
• Familiarity with big data technologies like Hadoop, Spark, or similar.
• Proficient in SQL and experience with database management.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.

• Experience with natural language processing (NLP), computer vision, or reinforcement learning.
• Knowledge of deep learning architectures and techniques.
• Experience with version control systems such as Git.
• Familiarity with containerization technologies like Docker and Kubernetes.
• Experience in deploying and maintaining models in production environments using MLOps practices.

• Competitive salary and benefits package.
• Opportunity to work with a talented and passionate team.
• Continuous learning and professional development opportunities.
• Flexible work environment and remote work options.
• Chance to make a significant impact on innovative projects and initiatives.