Machine Learning Operations Engineer

As a Machine Learning Operations Engineer at our innovative data science start-up, you will be instrumental in bridging the gap between machine learning development and production. Your primary responsibility will be to manage the operational lifecycle of machine learning models, ensuring they are built, maintained, and optimised for peak performance. You will focus on deploying, scaling, and monitoring ML models to guarantee their seamless and reliable functionality in real-world production environments.

In this role, you will collaborate closely with Data Scientists, Data Engineers, Software Developers, IT operations staff, and business stakeholders to create robust workflows that facilitate efficient model deployment and integration. Your expertise will help streamline processes, improve model performance, and ensure that the solutions we deliver meet business objectives and user needs.

By leveraging your skills in automation, version control, and cloud technologies, you will contribute to the development of scalable and maintainable machine learning systems. Join us in driving innovation and making a significant impact in the field of data science as we transform data into actionable insights.

The Day Job

• Developing and Maintaining ML Platforms: You will be responsible for developing and maintaining platforms and systems that automate the end-to-end machine learning pipeline, which encompasses building, training, testing, deploying, monitoring, and updating machine learning models.
• Implementing CI/CD Pipelines: Implement and maintain continuous integration and continuous deployment (CI/CD) pipelines specifically tailored for machine learning workflows, ensuring that models can be continuously updated and deployed without disruption.
• Seamless Model Deployment: Deploy machine learning models into production environments smoothly, making them accessible to applications and end-users while ensuring their reliability.
• Monitoring and Alerting Systems: Set up and manage monitoring and alerting systems to track the performance, health, availability, accuracy, and resource usage of deployed models, ensuring they operate effectively in real-time.
• Troubleshooting and Issue Resolution: Troubleshoot issues that arise in machine learning models or the supporting infrastructure, identifying patterns and resolving errors or bugs promptly.
• Optimising Applications and Infrastructure: Optimise applications and infrastructure for maximum speed, scalability, and efficiency, particularly when handling large volumes of data in production.
• Version Management: Manage different versions of machine learning models to maintain consistency and ensure that the correct version is in use across environments.
• Writing Clean Code: Write clean, maintainable, and reusable code primarily in Python for deployment, automation, and integration tasks.
• Collaboration with Data Teams: Collaborate closely with Data Scientists to effectively produce models and work with Data Engineers on data pipelines and quality assurance.
• IT Infrastructure Management: Work with IT infrastructure, including cloud environments, servers, storage, and networks, utilising tools such as Docker for deployment and orchestration.
• Documentation Creation: Create and maintain comprehensive documentation for deployment processes, optimisations, changes, and troubleshooting procedures to ensure knowledge sharing and operational continuity.
• Automated Model Retraining: Implement features for automated model retraining where necessary, ensuring models remain accurate and relevant over time.
• Ensuring Security and Compliance: Ensure platform security and compliance, maintaining awareness of common web vulnerabilities and security best practices to protect data and infrastructure.

Please speak to us if you have …..

…..the following professional aspirations

• Deepening Technical Expertise: Aspire to deepen your technical expertise in MLOps practices and master tools and technologies related to cloud platforms, containerisation, and automation.
• Career Advancement: Aim to progress to a senior MLOps engineer position or potentially transition into a technical architect or leadership role, taking on greater responsibilities and influencing the technical direction of projects.
• Designing Robust ML Systems: Be motivated to design and implement scalable, efficient, and robust machine learning systems that can effectively handle increasing data volumes and complexity.
• Collaborative Solution Development: Seek to work closely with Data Scientists, Data Engineers, and other stakeholders to understand their needs and deliver solutions that leverage machine learning models effectively.
• Mentorship Opportunities: Show interest in mentoring junior engineers and contributing to a collaborative team culture that creates growth and knowledge sharing.
• Aligning with Business Goals: Aim to align MLOps initiatives with business objectives, ensuring that the ML infrastructure supports the company’s strategic direction and contributes to overall success.
• Exploring Innovative Solutions: Be eager to explore and implement innovative data and machine learning solutions that enhance operational efficiency.
• Enhancing End-to-End ML Solutions: Maintain a passion for improving end-to-end solutions for machine learning in production, driving the success of deployed models.

…the following technical skills and knowledge

• Proficiency in Programming Languages: Strong proficiency in Python is essential, along with experience in Bash/Shell scripting. Familiarity with additional languages such as Java, Scala, R, or Go is a plus.
• Understanding of Machine Learning Fundamentals: A solid understanding of machine learning concepts, including algorithms, data pre-processing, model evaluation, and training. Familiarity with ML frameworks such as TensorFlow, PyTorch, and scikit-learn is beneficial.
• DevOps Practices: Experience with DevOps practices, including continuous integration and continuous deployment (CI/CD), containerisation using Docker, and Infrastructure as Code (IaC) methodologies.
• Cloud Platforms: Proficient in working with cloud platforms such as AWS, Azure, or Google Cloud for deploying and managing machine learning models and infrastructure.
• Data Management Knowledge: Understanding of data management principles, including experience with databases (SQL and NoSQL) and familiarity with big data frameworks like Apache Spark or Hadoop. Knowledge of data ingestion, storage, and management is essential.
• Monitoring and Logging Tools: Experience with monitoring and logging tools to track system performance and model effectiveness in production environments.
• Familiarity with MLOps Tools: Knowledge of various MLOps tools and platforms, including MLflow, Databricks, Kubeflow, and SageMaker, to streamline the machine learning lifecycle.
• Version Control Systems: Proficient in using version control systems such as Git to manage code and collaborate with development teams.
• Software Testing and Debugging: Experience in software testing and debugging practices to ensure code quality and reliability.
• Agile Environment Experience: Familiarity with working in Agile development environments, participating in sprints and collaborative planning.
• Model Deployment and Monitoring Techniques: Understanding of techniques for deploying and monitoring machine learning models to ensure they perform effectively in production.
• Web Security Awareness: Awareness of web security best practices and common vulnerabilities, ensuring that deployed solutions are secure.

…and the following experience, accreditations, and qualifications

• Education: A Bachelor’s degree in computer science, software engineering, data science, computational statistics, mathematics, or a related field is preferred. Equivalent professional experience may also be acceptable.
• Relevant Professional Experience: Significant professional experience in software development, DevOps, or machine learning roles is expected, as this position is not entry-level.
• Hands-On Project Experience: Demonstrable hands-on experience with projects related to building, deploying, and monitoring ML models is key. A portfolio showcasing your proficiency and relevant projects is beneficial.
• Scalable Data Pipeline Development: Experience in developing scalable data pipelines is highly relevant, contributing to the overall efficiency of ML workflows.
• Certifications: Relevant certifications in cloud platforms (AWS, Azure, Google Cloud), MLOps-specific certifications (such as Certified MLOps Engineer, Databricks Certified ML Professional), or related areas like DevOps or Machine Learning.