| Description | Description Position at ARRISE About Us:ARRISE sets the benchmark for service delivery and excellence in the iGaming industry. Playing a key role in the success of its clients which include Pragmatic Play a brand relied upon by the world’s biggest online casinos for its cutting-edge products ARRISE helps to deliver exceptional gaming experiences to millions of players worldwide. Our global team of over 10000 talented and driven professionals are shaping the future of iGaming. Headquartered in Gibraltar we have offices spanning Canada India the Isle of Man Latvia Malta Romania Serbia Bulgaria and the UAE and more exciting destinations on the horizon. At ARRISE we take pride in creating growth opportunities at all levels constantly investing in our people while welcoming new colleagues and forging strategic partnerships that open new opportunities for success. To achieve this we bet on ourselves. We know that success is a collective effort and our team is driven by ambition collaboration and a shared commitment to grow and succeed—while embracing every step of the journey. Be part of the future of iGaming with 10000 ARRISERS! See a job that excites you? Apply now and our friendly recruitment team will connect with you soon. Your journey starts here! The Role:We are seeking a talented and experienced MLOps Engineer that will work in a global team of Data Scientists delivering their methods into production in the most efficient way possible. You will also implement monitoring and alert systems. The ideal candidate should have in-depth knowledge and experience of model management and experiment tracking frameworks data storage and versioning Python Pytorch Azure Machine Learning Jenkins Grafana Gitlab CI and Docker. We are open to either Junior or Mid level MLOps Engineers for this exciting opportunity. Key Responsibilities:Collect preprocess manage and version datasets to support reliable and reproducible ML workflowsMaintain and automate data and training pipelines for continuous ingestion validation transformation and model retrainingBuild and manage CI/CD pipelines for application’s testing validation and deploymentDeploy ML models as scalable APIs using Docker FastAPI and AzureMLMonitor and maintain deployed APIs to ensure performance reliability and securityTrack model performance data drift and model drift using Prometheus Grafana and MLflowUse MLflow to manage the full ML lifecycle including training evaluation and versioningCreate and manage internal interfaces/tools to configure monitor and manage applications developed by the teamDevelop observability dashboards and alerting systems for model and infrastructure healthImplement unit and integration tests for ML code pipelines and deployment workflowsFollow security best practices in containerized deployments and data handlingCollaborate with data scientists and engineers to integrate models into production environments smoothlyRequired Skills And Qualifications:Bachelor's or Master's degree in Computer Science Engineering or a related field.Strong proficiency in Python.Extensive knowledge of key machine learning metrics and optimization techniques.Strong knowledge of version control systems (Git data versioning ...).Experience in model management and experiment tracking frameworks.Strong knowledge and practical understanding of different ML Ops techniques.Prior experience in handling very large datasets across different business functions.If you are a skilled Machine Learning Engineer with a passion for working in a fast-paced environment a keen eye for detail and a curiosity to experiment with new ideas we encourage you to apply and join our dynamic innovative team. What We Offer:Competitive compensation based on your experience and impact.Opportunities for professional and personal development.Work on state-of-the-art machine learning infrastructure and systems at scale.Opportunities to contribute to open-source projects and stay active in the ML community.Opportunity to make a measurable and visible impact within a large-scale organization.Flexible working hours and remote-friendly setup. |