Job description

Job Description

We are seeking a Senior Officer MLOps Integration Engineer to join our team. This role will be crucial in bridging the gap between data science and IT operations, focusing on the seamless integration, deployment, and monitoring of machine learning models within our enterprise systems.

Job Responsibilities:**

* Design, develop, and maintain robust MLOps pipelines for 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 teams to ensure efficient and reliable model integration into existing and new applications.
* Develop and implement automated testing, continuous integration, and continuous 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 and infrastructure.
* Ensure compliance with bank security and governance policies throughout the ML lifecycle.
* Research and implement new MLOps tools and best practices to optimize efficiency and reliability.
* Document MLOps processes, architectures, and solutions for maintainability and 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 machine learning 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 cloud platforms (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 lifecycle management.
* Experience with data engineering concepts and tools (e.g., SQL, Spark, Kafka).
* Excellent problem-solving skills and the ability to work independently and as part of a team.
* Strong communication and interpersonal skills.
* Experience in the financial services industry is a plus.

Additional Requirements