Mlops Integration Engineer

Job Description We are seeking a Senior Officer MLOps IntegrationEngineer to join our team. This role will be crucial in bridging the gapbetween data science and IT operations, focusing on the seamless integration,deployment, and monitoring of machine learning models within our enterprisesystems. Job Responsibilities:** * Design, develop, and maintain robust MLOps pipelinesfor model training, deployment, monitoring, and retraining. * Implement and manage infrastructure for scalable machine learning workloads,including containerization (e.g., Docker, Kubernetes) and cloud platforms(e.g., AWS, Azure, GCP). * Collaborate with data scientists, software engineers, and IT operations teamsto ensure efficient and reliable model integration into existing and newapplications. * Develop and implement automated testing, continuous integration, andcontinuous deployment (CI/CD) strategies for ML models. * Establish and maintain monitoring and alerting systems for model performance,data drift, and operational health. * Troubleshoot and resolve issues related to ML model deployments andinfrastructure. * Ensure compliance with bank security and governance policies throughout theML lifecycle. * Research and implement new MLOps tools and best practices to optimizeefficiency and reliability. * Document MLOps processes, architectures, and solutions for maintainabilityand knowledge sharing. Job Qualifications:** * Bachelor's or Master's degree in Computer Science,Engineering, Data Science, or a related quantitative field. * 3+ years of experience in MLOps, DevOps, or a similar role focused on machinelearning systems. * Strong programming skills in Python is essential. * Proficiency with MLOps platforms and tools (e.g., MLflow, Kubeflow,Sagemaker, Azure ML, Vertex AI). * Experience with containerization technologies (Docker, Kubernetes) and cloudplatforms (AWS, Azure, GCP). * Solid understanding of CI/CD principles and tools (e.g., Jenkins, GitLab CI,Azure DevOps). * Familiarity with machine learning concepts, algorithms, and model lifecyclemanagement. * Experience with data engineering concepts and tools (e.g., SQL, Spark,Kafka). * Excellent problem-solving skills and the ability to work independently and aspart of a team. * Strong communication and interpersonal skills. * Experience in the financial services industry is a plus.