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- Tasks: Design and manage MLOps pipelines for machine learning deployment using Azure.
- Company: Join a leading FinTech company revolutionising digital finance with innovative tech solutions.
- Benefits: Enjoy remote/hybrid work options and potential contract extension after 6 months.
- Why this job: Be part of a dynamic team transforming finance while working on cutting-edge technology.
- Qualifications: 3+ years in MLOps or Cloud Engineering, strong Azure expertise required.
- Other info: Opportunity to work with data scientists and enhance your skills in a fast-paced environment.
The predicted salary is between 48000 - 72000 £ per year.
Location: Remote / Hybrid (Preferred)
Contract Type: 6-Month Contract (with potential for extension)
Start Date: ASAP
About Us: We are a leading FinTech company transforming digital finance through cutting-edge technology and innovative solutions. As we scale our data and machine learning (ML) capabilities, we are seeking an experienced MLOps Engineer to join our dynamic team. In this role, you will bridge the gap between machine learning models and production-ready systems, leveraging Azure to deploy, monitor, and automate our ML pipelines.
Key Responsibilities:
- Design, implement, and manage end-to-end MLOps pipelines for machine learning model deployment, monitoring, and automation using Azure Machine Learning (AML).
- Leverage Azure DevOps for creating CI/CD pipelines for model training, validation, deployment, and version control.
- Develop robust data pipelines using Azure Data Factory and Azure Databricks for ML workflows.
- Deploy machine learning models to Azure Kubernetes Service (AKS), Azure Container Instances (ACI), or Azure Functions.
- Implement monitoring and logging solutions for ML models using Azure Monitor, Log Analytics, and Application Insights.
- Ensure models are compliant with FinTech industry standards, addressing performance, security (Security API, Secret Management), and auditability.
- Work closely with data scientists and software engineers to integrate and optimise models into production systems.
- Provide training and documentation on maintaining the MLOps pipelines and monitoring systems.
Key Deliverables:
- End-to-End MLOps Pipeline: Fully automated pipeline that incorporates model development, versioning, deployment, and monitoring using Azure Machine Learning, Azure DevOps, and Terraform.
- Model Deployment Framework: Deployment of machine learning models to production environments on Azure Kubernetes Service (AKS), ACI, or Azure Functions, ensuring scalability and high availability.
- Automated Data Pipelines: Integration of data pipelines with Azure Data Factory and Azure Synapse for robust ML data management and preprocessing.
- Real-Time Monitoring: Implementation of real-time monitoring and alerts for model performance and drift using Azure Monitor, Application Insights, and Log Analytics.
- Model Versioning and Governance: Setup of model version control, automated workflows for model retraining, and performance tracking through AML and Azure DevOps.
- Documentation & Knowledge Sharing: Creation of comprehensive documentation for MLOps pipelines and deployment processes to ensure seamless handover and transparency.
Required Skills & Experience:
- 3+ years of hands-on experience in MLOps, DevOps, or Cloud Engineering roles, with a strong emphasis on Azure environments.
- Expertise in Azure Machine Learning (AML), including model management, AutoML, model deployment, and experiment tracking.
- Experience in building and maintaining CI/CD pipelines using Azure DevOps and version control systems like Git.
- Strong knowledge of Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and Azure Functions for containerised model deployment.
- Proven experience with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for managing and orchestrating data pipelines.
- Familiarity with Terraform for infrastructure as code (IaC) and automated provisioning of Azure resources.
- Proficient in Python, with experience in scikit-learn, TensorFlow, PyTorch, or XGBoost for machine learning tasks.
- Solid understanding of model monitoring and logging using Azure Monitor, Application Insights, and Log Analytics.
- Knowledge of best practices for model governance, versioning, and deployment in a regulated FinTech environment.
Nice to Have:
- Azure certifications (e.g., Azure AI Engineer, Azure Solutions Architect).
- Experience with MLflow, Kubeflow, or other model management frameworks.
- Familiarity with containerisation and orchestration tools like Docker, Kubernetes, and Helm.
- Experience in data security and compliance standards relevant to the FinTech industry, including PCI DSS or SOC 2 compliance.
MLOps Engineer – Azure employer: Innovate Cloud LTD
Contact Detail:
Innovate Cloud LTD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer – Azure
✨Tip Number 1
Familiarise yourself with Azure Machine Learning and its features. Understanding how to leverage AML for model management and deployment will give you a significant edge during interviews.
✨Tip Number 2
Brush up on your CI/CD pipeline skills using Azure DevOps. Being able to discuss your experience in building and maintaining these pipelines will demonstrate your hands-on expertise.
✨Tip Number 3
Showcase any projects where you've implemented monitoring solutions using Azure Monitor or Application Insights. Real-world examples can help illustrate your capability to ensure model performance and compliance.
✨Tip Number 4
If you have experience with Terraform, be ready to discuss how you've used it for infrastructure as code. This knowledge is particularly relevant for automating Azure resource provisioning.
We think you need these skills to ace MLOps Engineer – Azure
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, Azure environments, and any specific tools mentioned in the job description. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your qualifications but also demonstrates your passion for FinTech and how your skills can contribute to the company's goals. Mention specific projects or experiences that align with the responsibilities of the role.
Showcase Relevant Projects: If you have worked on projects involving Azure Machine Learning, CI/CD pipelines, or data management, be sure to include these in your application. Provide brief descriptions of your role and the impact of your work.
Highlight Continuous Learning: Mention any relevant certifications or courses you have completed, especially those related to Azure or MLOps. This shows your commitment to staying updated in the field and enhances your credibility as a candidate.
How to prepare for a job interview at Innovate Cloud LTD
✨Showcase Your Azure Expertise
Make sure to highlight your hands-on experience with Azure Machine Learning and other Azure services. Be prepared to discuss specific projects where you've implemented MLOps pipelines, as this will demonstrate your capability to bridge the gap between machine learning models and production systems.
✨Demonstrate CI/CD Knowledge
Since the role involves creating CI/CD pipelines using Azure DevOps, be ready to explain your experience with version control systems like Git. Discuss how you've set up automated workflows for model training, validation, and deployment in previous roles.
✨Prepare for Technical Questions
Expect technical questions related to Azure Kubernetes Service, Azure Data Factory, and data pipeline management. Brush up on your knowledge of these tools and be ready to provide examples of how you've used them to solve real-world problems.
✨Understand FinTech Compliance
Familiarise yourself with the compliance standards relevant to the FinTech industry, such as PCI DSS or SOC 2. Be prepared to discuss how you ensure that machine learning models are compliant with these standards in your work.
