Your key responsibilities

• Develop and deploy machine learning models on Azure cloud platform using Python programming language, ensuring scalability and efficiency.
• Design and implement scalable and efficient data pipelines for model training and inference, optimizing data processing workflows.
• Collaborate closely with data scientists and business stakeholders to understand requirements, translate them into technical solutions, and deliver high-quality ML solutions.
• Implement best practices for ML development, including version control using tools like Git, testing methodologies, and documentation to ensure reproducibility and maintainability.
• Design and optimize ML algorithms and data structures for performance and accuracy, leveraging Azure cloud services and Python libraries such as TensorFlow, PyTorch, or scikit-learn.
• Monitor and evaluate model performance, conduct experiments, and iterate on models to improve predictive accuracy and business outcomes.
• Work on feature engineering, data preprocessing, and feature selection techniques to enhance model performance and interpretability.
• Collaborate with DevOps teams to deploy ML models into production environments, ensuring seamless integration and continuous monitoring.
• Stay updated with the latest advancements in ML, Azure cloud services, and Python programming, and apply them to enhance ML capabilities and efficiency.
• Provide technical guidance and mentorship to junior developers and data scientists, fostering a culture of continuous learning and innovation.

Skills and attributes
Soft Skills

• Bachelor's or master's degree in computer science, data science, or related field, with a strong foundation in ML algorithms, statistics, and programming concepts.
• Minimum 6-8 years of hands-on experience in developing and deploying ML models on Azure cloud platform using Python programming language.
• Expertise in designing and implementing scalable data pipelines for ML model training and inference, utilizing Azure Data Factory, Azure Databricks, or similar tools.
• Proficiency in Python programming language, including libraries such as TensorFlow, PyTorch, scikit-learn, pandas, and NumPy for ML model development and data manipulation.
• Strong understanding of ML model evaluation metrics, feature engineering techniques, and data preprocessing methods for structured and unstructured data.
• Experience with cloud-native technologies and services, including Azure Machine Learning, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
• Familiarity with DevOps practices, CI/CD pipelines, and containerization tools like Docker for ML model deployment and automation.
• Excellent problem-solving skills, analytical thinking, and attention to detail, with the ability to troubleshoot and debug complex ML algorithms and systems.
• Effective communication skills, both verbal and written, with the ability to explain technical concepts to non-technical stakeholders and collaborate in cross-functional teams.
• Proactive and self-motivated attitude, with a passion for learning new technologies and staying updated with industry trends in ML, cloud computing, and software development.
• Strong organizational skills and the ability to manage multiple projects, prioritize tasks, and deliver results within project timelines and specifications.
• Business acumen and understanding of the impact of ML solutions on business operations and decision-making processes, with a focus on delivering value and driving business outcomes.
• Collaboration and teamwork skills, with the ability to work effectively in a global, diverse, and distributed team environment, fostering a culture of innovation and continuous improvement..

To qualify for the role, you must have

• A bachelor's or master's degree in computer science, data science, or related field, along with a minimum of 6-8 years of experience in ML development and Azure cloud platform expertise.
• Strong communication skills and consulting experience are highly desirable for this position.

Ideally, you’ll also have

• Analytical ability to manage complex ML projects and prioritize tasks efficiently.
• Experience operating independently or with minimal supervision, demonstrating strong problem-solving skills.
• Familiarity with other cloud platforms and technologies such as AWS, Google Cloud Platform (GCP), or Kubernetes is a plus.