Engineer - Machine Learning Ops

Job Summary

You will be responsible for designing, building, and maintaining scalable machine learning pipelines, deploying models to production environments, and ensuring the reliability and scalability of ML operations. The role involves managing infrastructure, implementing CI/CD pipelines, containerization, API management, monitoring, security, collaboration with data scientists, and performance optimization.

Reporting Structure

This job reports to the Manager – AI

Job Objectives

Design, build, and maintain scalable ML pipelines and deploy models to test and production environments
Set up and manage cloud and on-premises infrastructure to support ML operations
Develop and maintain CI/CD pipelines for ML models and automate build, test, and deployment processes
Utilize Docker and Kubernetes for deploying ML models and manage containers for smooth operation and scalability
Develop and manage APIs to support ML models, monitor and secure API calls, and ensure seamless integration with external applications

Job Responsibilities

Pipeline & APIs Deployment and Management

Design, build, and maintain scalable machine learning pipelines to ensure efficient data processing and model deployment
Develop and manage APIs to support machine learning models and services
Ensure seamless integration between machine learning models and external applications
Utilize API management tools to monitor and secure API calls, enforcing access control and data protection measures
Deploy machine learning models to various environments, including testing and production, ensuring seamless integration and functionality
Ensure the reliability, availability, and scalability of ML pipelines by implementing robust monitoring and alerting systems
Provision pipeline operations effectively, managing resources such as compute, storage, and networking to optimize performance and cost-efficiency

(CI/CD) Implementation & Containerization

Develop and maintain CI/CD pipelines tailored for ML models and applications
Automate the build, test, and deployment processes
Utilize containerization technologies such as Docker and Kubernetes for deploying ML models, ensuring consistency and portability across environments
Manage and orchestrate containers effectively to optimize resource utilization and maintain scalability

Performance Monitoring and Optimization

Implement comprehensive monitoring and logging solutions to track the performance of ML models and pipelines, enabling proactive issue detection and resolution
Set up robust alerting systems to detect and respond to issues and anomalies promptly, minimizing downtime and performance degradation
Ensure compliance with security standards and regulations, implementing measures to protect data privacy and model security
Continuously monitor and optimize the performance of ML models and infrastructure, identifying and resolving bottlenecks to improve system efficiency
Respond to and resolve incidents related to ML operations promptly

Scalability and Resource Optimization

Set up and manage both cloud and on-premises infrastructure to support ML operations
Optimize models and infrastructure for performance and scalability in production environments, ensuring efficient and reliable operations
Manage resource allocation to ensure cost-effective operations
Develop scripts and automation tools to streamline ML operations, automating repetitive tasks to improve operational efficiency

Disaster Recovery and Incident Report

Implement backup and disaster recovery plans for ML models and data
Ensure data and model availability in case of failures
Conduct root cause analysis and implement preventive measures to mitigate future occurrences

Collaboration and Best Practices

Collaborate closely with data scientists and engineers throughout the ML lifecycle, from model development, and testing to deployment and maintenance
Collaborate with data scientists and AI researchers to develop and test machine learning models
Provide support and guidance on best practices for ML operations, facilitating effective teamwork and knowledge sharing
Implement best practices for model versioning, testing, and validation

Job Requirements

Educational Qualification

Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field

Previous Work Experience

4 years of proven experience as an ML Ops Engineer or similar role in a production environment
Experience with Azure cloud platform. AWS experience is a plus
Experience with containerization technologies (Docker, Kubernetes)
Experience with API management tools (Kong)

Skills And Abilities

Strong programming skills in Python
Proficiency in CI/CD tools
Familiarity with machine learning frameworks (TensorFlow, PyTorch)
Strong understanding of DevOps practices and principles
Excellent problem-solving skills and attention to detail
Strong communication and collaboration skills

Engineer - Machine Learning Ops

Key Skills

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