Machine Learning Engineer

Keyrus is an international consulting firm, specializing in the integration of data intelligence and Digital solutions. With over 3000 employees spread across 20 countries, Keyrus continues to deliver on such projects to a wide range of clients from various industries including but not limited to Banking/Finance, Healthcare/pharmaceuticals, FMCG, Oil & Gas, and more.

As part of Keyrus’ solution delivery, we are also in a position to recruit and place technical consultants to complement on existing client projects with their expertise. As such, we seek innovative and agile people to support ambitious and forthcoming technological challenges.

Our team in Singapore is currently looking for a ML Ops Engineer, in order to support our client activities.

Main responsibilities

• Collaborate with data scientists and software engineers to Implement and optimize generative AI and machine learning models in production environments.
• Provision & manage the infrastructure required for hosting ML models, including cloud resources and on-premises servers.
• Utilize containerization technologies like Docker to package models and dependencies.
• Develop and maintain CI/CD pipelines for automating the testing, integration, and deployment of ML models.
• Implement version control to track changes in both code and model artifacts.
• Optimize existing models for performance and scalability.
• Establish monitoring solutions to track the performance and health of deployed models.
• Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
• Ensure compliance with group regulations and data protection standards.
• Document processes, methodologies, and results for transparency and reproducibility.

Skills required

• 7+ years of IT experience
• Architecture and deployment of solutions on cloud platforms like Azure/AWS or GCP.
• Proficient in machine learning algorithms, models, and statistical concepts.
• Knowledge of data preprocessing techniques and feature extraction.
• Experience with generative AI tools (e.g., Microsoft OpenAI, Domino Data Lab).
• Skilled in MLOps frameworks (e.g., Kubeflow, MLFlow, DataRobot, Airflow).
• Docker and Kubernetes, OpenShift for container management and scaling.
• Proficient in programming languages (Python, Go, Ruby, Bash) and Linux.
• Familiarity with machine learning frameworks (scikit-learn, Keras, PyTorch, TensorFlow).
• Experience with CI/CD pipelines.
• Experience with Mistral models and other machine learning frameworks.

Nice to have Qualifications:

• Strong knowledge on API Management and Integration using APIGEE API Management platform
• Domino Datalab certification
• MLOps Certification
• Working knowledge of Scrum framework