Machine Learning Operations Engineer

4 weeks ago

Noida Bengaluru Bangalore, India Foundit : formerly Monster (APAC & ME) Full time
As a Machine Learning Operations Engineer at foundit, you will play a crucial role in bridging the gap between data science and production deployment. You will be responsible for managing and optimizing the machine learning model deployment pipeline, ensuring the scalability, reliability, and efficiency of our machine learning systems. If you are passionate about enabling data scientists to deliver their models to production and have a strong background in machine learning operations, we want to hear from you

Key Responsibilities:

• Design, implement, and maintain the end-to-end machine learning operational pipeline, including model development, deployment, monitoring, and maintenance.
• Collaborate with data scientists, software engineers, and DevOps teams to integrate machine learning models into production systems, ensuring scalability, reliability, and performance.
• Establish and enforce best practices for code quality, version control, and deployment strategies to ensure the stability and maintainability of machine learning systems.
• Implement CI/CD pipeline for robust monitoring and alerting mechanisms to track model performance, data quality, and system health, and take proactive measures to address any issues that arise.
• Ensure robust, scalable, and cost-effective deployment of models on AWS, Azure, or GCP (Azure preferred).
• Containerize machine learning applications using Docker for seamless deployment and scaling.
• Continuously research and evaluate emerging tools, technologies, and methodologies in the MLOps space to drive innovation and improve overall efficiency.
•

Qualifications:
• Proven experience as an MLOps Engineer or similar role, with a minimum of 3 years of hands-on experience in deploying and managing machine learning models in production environments.
• Strong proficiency in programming languages such as Python, along with experience working with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Solid understanding of cloud platforms (e.g., IBM, AWS, Azure, GCP) and experience deploying machine learning models in cloud environments.
• Proficiency in MLflow/Kubeflow for managing machine learning workflows.
• Expertise in containerization technologies (e.g., Docker, Kubernetes) and experience with container orchestration for scalable and efficient deployment.
• In-depth knowledge of DevOps practices and tools, including CI/CD pipelines, version control systems (e.g., Git)
• Experience with data engineering, ETL pipelines, and distributed data processing frameworks (e.g., Apache Spark) for handling large-scale datasets.
• Strong problem-solving and troubleshooting skills, with the ability to analyze complex systems and resolve issues efficiently.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and present complex ideas to both technical and non-technical stakeholders.

Preferred Qualifications:
- Knowledge of infrastructure as code (IaC) and automation tools such as Terraform or Ansible.
- Relevant certifications in machine learning operations or cloud platforms.
- Experience with PySpark would be a bonus

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