ML Ops Engineer

ZettaMine Labs Pvt. Ltd.; Chennai District; On-site; INR 3,000,000 - 4,200,000; Full time

Job summary

ZettaMine Labs Pvt. Ltd. in Chennai seeks an experienced ML Ops Senior Engineer to lead deployment, monitoring, and management of ML models in production.

You will work with data scientists, software engineers, and DevOps to optimize the ML pipeline, ensure scalability, and maintain high availability of ML applications. The role requires strong ML and data engineering fundamentals, software engineering practices, and DevOps proficiency.

Qualifications

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

Responsibilities

• Lead design and implementation of ML Ops solutions for production deployment.
• Collaborate with data engineers, scientists, and developers to integrate data pipelines with ML models.
• Implement and maintain IaC for ML infrastructure and data storage.
• Monitor model performance and troubleshoot data-related issues in production.
• Ensure security and regulatory compliance in ML workflows.
• Collaborate to optimize end-to-end ML pipeline and data-driven initiatives.
• Stay updated on ML Ops and DevOps trends and train the team.
• Mentor junior team members and foster innovation.

Skills

Problem solving

Team collaboration

Communication

Education

Bachelor's or Master's in CS/Engineering

Tools

Python

TensorFlow

PyTorch

Scikit-learn

API Integration

Web Frameworks

Flask

FastAPI

Docker

Kubernetes

AWS

Azure

GCP

CI/CD

Git

Jenkins

Bitbucket Pipeline

Terraform

Ansible

Data Modeling

ETL

Data Warehousing

Vertex AI

Datadog

Google Cloud ML Engineer

Job description

We are seeking a highly skilled and experienced ML Ops Senior Engineer with a strong background in data engineering to join our dynamic team. The ideal candidate will be responsible for leading and implementing the deployment, monitoring, and management of machine learning models in production environments while also possessing expertise in data engineering principles.

This role requires a deep understanding of both machine learning and data engineering concepts, as well as proficiency in software engineering and DevOps practices. The ML Ops Senior Engineer will collaborate closely with data scientists, software engineers, and DevOps professionals to optimize the end-to-end ML pipeline, ensure model scalability, and maintain high availability of ML applications.

Responsibilities

• Lead the design, development, and implementation of ML Ops solutions to deploy machine learning models into production environments efficiently, leveraging data engineering best practices.
• Collaborate with data engineers, data scientists, and software engineers to integrate data pipelines with ML models, including model versioning, model and data lineage monitoring, model hosting and deployment scalability, orchestration, continuous training, deployment, and automated pipelines with best practices, ensuring data quality, reliability, and scalability.
• Implement and maintain Infrastructure as Code (IaC) solutions for provisioning, configuring, and managing ML infrastructure, including data storage and processing systems.
• Monitor model performance, resource utilization, and system health in production environments and troubleshoot data-related issues as they arise.
• Implement security measures and compliance standards to protect sensitive data and ensure regulatory compliance in ML workflows, collaborating with data governance and security teams.
• Collaborate with cross-functional teams to optimize the end-to-end ML pipeline, improve model scalability, and enhance system reliability while also contributing data engineering expertise to data-driven initiatives.
• Stay updated on emerging trends, technologies, and best practices in ML Ops, DevOps, data engineering, and machine learning domains and share knowledge with the team.
• Mentor junior team members, provide technical guidance, and contribute to the continuous learning and development of the team, fostering a culture of innovation and collaboration.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 4 years of experience in software engineering, DevOps, or a related field, with a focus on deploying and managing machine learning models in production environments, combined with a strong background in data engineering.
• Strong proficiency in programming languages such as Python and experience with software development frameworks and libraries, e.g., TensorFlow, PyTorch, Scikit-learn, API Integration, Web Frameworks, Flask, or FastAPI.
• Experience with containerization technologies, e.g., Docker, Kubernetes, and cloud platforms, e.g., AWS, Azure, GCP.
• Hands-on experience with CI/CD pipelines, version control systems such as Git, and automation tools such as Jenkins and Bitbucket Pipeline.
• Familiarity with Infrastructure as Code (IaC) tools, e.g., Terraform, Ansible, and configuration management systems.
• Solid understanding of machine learning concepts, algorithms, and model evaluation metrics, as well as data engineering principles such as data modeling, ETL processes, and data warehousing.
• Experience with monitoring and logging tools, e.g., Vertex AI, Datadog, for tracking system performance and diagnosing data-related issues.
• Excellent problem-solving skills, attention to detail, and ability to work effectively in a fast-paced environment.
• Strong communication and interpersonal skills with the ability to collaborate effectively with cross-functional teams and stakeholders.
• Certification in cloud computing, e.g., Google Cloud Professional Machine Learning Engineer.

Skills

Mandatory Skills: MLOPS, MLOPS – Python