Your key responsibilities

• Develop and deploy machine learning models on Azure cloud platform using Python programming language and Azure ML services.
• Design and implement scalable and efficient data pipelines for model training and inference.
• Collaborate with data scientists and business stakeholders to understand requirements and translate them into technical solutions.
• Implement best practices for ML development, including version control, testing, and documentation.
• Develop and maintain APIs on Azure cloud platform for model deployment and integration with applications.
• Monitor and optimize model performance, ensuring scalability, reliability, and security.
• Participate in code reviews, troubleshooting, and optimization to improve performance and maintainability.
• Stay updated with industry trends and best practices in machine learning, cloud computing, and API development.
• Mentor junior team members, sharing expertise and guiding them in technical challenges.
• Collaborate with cross-functional teams, including data engineers, data scientists, and business analysts, to deliver end-to-end solutions.
• Conduct regular evaluations and assessments of ML models and APIs, identifying areas for improvement and optimization.
• Lead and contribute to innovation initiatives, exploring new technologies and approaches to enhance our ML and cloud capabilities.

Soft Skills

• Bachelor's degree in computer science, data science, or related field.
• 6-8 years of experience in machine learning, data engineering, and cloud computing, with a strong focus on Azure services.
• Proficiency in Python programming language for ML model development and deployment.
• Experience with Azure ML services, Azure data services (e.g., Azure Data Lake, Azure SQL Database), and Azure cloud infrastructure.
• Strong understanding of machine learning concepts, algorithms, and techniques, including supervised and unsupervised learning, deep learning, and natural language processing.
• Expertise in designing and implementing data pipelines for ML model training and inference, using tools like Azure Databricks, Azure Data Factory, and Azure Synapse Analytics.
• Familiarity with cloud-native technologies, containerization (e.g., Docker, Kubernetes), and DevOps practices for ML model deployment and automation.
• Experience with version control systems (e.g., Git/GitHub), CI/CD pipelines, and software engineering best practices for ML projects.
• Strong analytical and problem-solving skills, with the ability to analyze complex data sets, identify patterns, and derive meaningful insights.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.
• Proactive mindset, self-driven, and eager to learn new technologies and methodologies in the ML and cloud computing space.
• Ability to work in a fast-paced environment, manage multiple priorities, and deliver high-quality results under tight deadlines.