About this role
<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Job Summary</b></u></H2> </div><div><p style="text-align:justify"><span style=""><span style=""><span style="">The</span> <span style=""><span><span style="">MLOps Engineer is responsible for integrating machine learning models into all environments efficiently and effectively. This role combines elements of machine learning, software engineering, and operations to ensure that models are deployed reliably and can scale to meet demand. MLOps Engineers build, maintain, and optimize machine learning solutions and ensure that ALL algorithms are performing as expected. </span></span></span></span></span></p> </div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Job Responsibilities 1</b></u></H2> </div><div><p style="text-align:justify"><span style=""><span style="">Develop and maintain pipelines for deploying machine learning models into our environments by automating the deployment process (CI/CD).</span></span></p> <p style="text-align:justify"><span style=""><span style="">Implement monitoring systems to track the performance and health of deployed models and establish alerting mechanisms to detect any anomalies or performance degradation and make necessary adjustments.</span></span></p> <p style="text-align:justify"><span style=""><span style="">Documentation and reproducibility to ensure that experiments and models are reproducible and well-documented.</span></span></p> <p style="text-align:justify"><span style=""><span style="">Manage machine learning model versioning to ensure smooth transitions between versions. Maintain clear documentation of model configurations, dependencies, and deployment processes to facilitate seamless updates and operations.</span></span></p> </div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Job Responsibilities 2</b></u></H2> </div><div><p style="text-align:justify"><span style=""><span style="">Optimize models and deployment pipelines to enhance performance and scalability by focusing on efficient load balancing and effective resource management.</span></span></p> <p style="text-align:justify"><span style=""><span style="">Manage the infrastructure for deploying and operating models, whether on the cloud or on-premises. Make sure it's secure, cost-effective, and meets the needs of your machine-learning models.</span></span></p> <p style="text-align:justify"><span style=""><span style="">Oversee data pipelines to ensure high-quality and consistent data for training and inference. Implement practices for data versioning and management</span></span></p> <p style="text-align:justify"><span style=""><span style="">Ensures security in all MLOps processes considering, data protection, deployment security, regulatory compliance, and model integrity.</span></span></p> </div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><b>Additional Responsibilities 3</b></H2> </div><div></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Job Knowledge & Skills</b></u></H2> </div><div><p style="border:none;text-align:justify"><span style=""><span><span style=""><span style="">Comprehensive understanding of the end-to-end machine learning lifecycle, including model training, validation, and deployment.</span></span></span></span></p> <p style="border:none;text-align:justify"><span style=""><span><span style=""><span style="">Knowledge of different model deployment strategies and tools (e.g., REST APIs, model servers)</span></span></span></span></p> <p style="border:none;text-align:justify"><span style=""><span><span style=""><span style="">Understanding of techniques for scaling machine learning models and optimizing performance.</span></span></span></span></p> <p style="border:none;text-align:justify"><span style=""><span><span style=""><span style="">Familiarity with data engineering concepts and tools for handling large datasets and building data pipelines</span></span></span></span></p> <p style="border:none;text-align:justify"><span style=""><span><span><span style=""><span style="">Knowledge of DevOps practices and how they apply to machine learning workflows.</span></span></span></span></span></p> <p style="text-align:justify"><span style=""><span><span style=""><span style="">Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent).</span></span></span></span></p> <p style="border:none;text-align:justify"><span style=""><span style="Arial', sans-serif">ERP knowledge preferably SAP functional skills is a requirement to be successful in this role </span></span></p> </div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Job Experience</b></u></H2> </div><div><p><span style=""><span style=""><span style="">Minimum 5 years working experience, 3 years relevant working experience, 2 years GCC experience is a plus</span></span></span></p> </div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Competencies</b></u></H2> </div><div><div>Agility</div><div>AI Fluency</div><div>Artificial Intelligence Concepts L3</div><div>Build High-Performing Teams</div><div>Cloud Architecture L3</div><div>IT Security L3</div><div>Leadership</div><div>Machine Learning Algorithms L3</div><div>Product and Service Knowledge L3</div><div>Provide Direction</div><div>Quality</div><div>Resilience</div></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px"><u><b>Education</b></u></H2> </div><div><div>Bachelor's Degree in Information Technology</div></div></div></div>