DE-Azure ML And AI Engineer

Your key responsibilities

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

Soft Skills

1. Bachelor's degree in computer science, data science, or related field.
2. 6-8 years of experience in machine learning, data engineering, and cloud computing, with a strong focus on Azure services.
3. Proficiency in Python programming language for ML model development and deployment.
4. Experience with Azure ML services, Azure data services (e.g., Azure Data Lake, Azure SQL Database), and Azure cloud infrastructure.
5. Strong understanding of machine learning concepts, algorithms, and techniques, including supervised and unsupervised learning, deep learning, and natural language processing.
6. 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.
7. Familiarity with cloud-native technologies, containerization (e.g., Docker, Kubernetes), and DevOps practices for ML model deployment and automation.
8. Experience with version control systems (e.g., Git/GitHub), CI/CD pipelines, and software engineering best practices for ML projects.
9. Strong analytical and problem-solving skills, with the ability to analyze complex data sets, identify patterns, and derive meaningful insights.
10. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.
11. Proactive mindset, self-driven, and eager to learn new technologies and methodologies in the ML and cloud computing space.
12. Ability to work in a fast-paced environment, manage multiple priorities, and deliver high-quality results under tight deadlines.

To qualify for the role, you must have

1. A bachelor's or master's degree in computer science, data science, or related field.
2. 6-8 years of hands-on experience in machine learning, cloud computing, and API development, with a focus on Azure services.
3. Proficiency in Python programming language for ML model development and deployment, along with experience using Azure ML services and Azure cloud infrastructure.
4. Strong communication skills, both verbal and written, with the ability to articulate technical concepts effectively to diverse audiences.
5. Proven track record of successfully delivering ML projects, designing scalable data pipelines, and collaborating in cross-functional teams.
6. Certifications such as Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Data Scientist Associate, or equivalent, are highly desirable.
7. Experience with other cloud platforms (e.g., AWS, Google Cloud Platform) and additional programming languages (e.g., R, Java) is a plus.

Ideally, you’ll also have

1. Analytical ability to manage multiple ML projects simultaneously, prioritize tasks effectively, and make data-driven decisions.
2. Experience operating independently or with minimal supervision, demonstrating strong problem-solving skills and initiative.
3. Familiarity with advanced ML techniques and frameworks (e.g., TensorFlow, PyTorch) for deep learning and computer vision applications.
4. Knowledge of cloud security principles, data governance, and regulatory compliance frameworks relevant to ML and cloud environments.
5. Experience with Big Data technologies (e.g., Apache Spark, Hadoop) for processing large-scale datasets and building ML models at scale.