Role Overview :

As a Machine Learning Platform Engineer at Zinnov, you will be instrumental in designing, developing, and maintaining our cutting-edge machine learning platform. You will collaborate closely with data scientists, machine learning engineers, and product managers to build scalable and reliable infrastructure that supports the entire machine learning lifecycle, from data ingestion and model training to deployment and monitoring. Your work will directly impact the efficiency and effectiveness of our machine learning initiatives, enabling us to deliver impactful solutions to our clients.

Key Responsibilities :

- Design and implement scalable and robust machine learning infrastructure to support the development and deployment of machine learning models.

- Build and maintain CI/CD pipelines for automated model training, testing, and deployment, ensuring rapid iteration and high-quality releases.

- Develop and maintain data pipelines for efficient data ingestion, processing, and storage, enabling data scientists to access and utilize data effectively.

- Monitor and optimize the performance of machine learning models in production, identifying and resolving issues to ensure optimal performance and reliability.

- Collaborate with data scientists and machine learning engineers to understand their requirements and provide them with the tools and infrastructure they need to succeed.

- Evaluate and integrate new technologies and tools into the machine learning platform, keeping it up-to-date with the latest advancements in the field.

- Document the machine learning platform architecture, design, and implementation, ensuring that it is well-understood and maintainable.

Required Skillset :

- Demonstrated ability to design, develop, and deploy machine learning platforms using cloud-based technologies such as AWS, Azure, or GCP.

- Proven expertise in building and maintaining CI/CD pipelines for machine learning models using tools such as Jenkins, GitLab CI, or CircleCI.

- Strong proficiency in programming languages such as Python, Java, or Scala, with experience in developing data pipelines using frameworks such as Apache Spark or Apache Beam.

- Solid understanding of machine learning algorithms and techniques, with experience in deploying models using frameworks such as TensorFlow, PyTorch, or scikit-learn.

- Excellent communication and collaboration skills, with the ability to work effectively with data scientists, machine learning engineers, and product managers.

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

- 5-8 years of relevant experience.