MLOps Engineer

Description

Global player in the railway sector is looking to create an internal platform aimed to support the Bid and Sales teams during critical stages of the bidding process, as well as to assist to Engineers throughout key phases of the Engineering projects lifecycle.

This solution utilizes various techniques, including multi-agent GenAI, to enhance the requirements and compliance process, facilitate data analysis and tender authoring, provide guidance in engineering, offer technical support, and aid in decision-making. It also generates essential artifacts such as requirements, designs, and tests. Additionally, the solution will enable data analytics to identify trends across bids, supporting the Sales and Product Development teams.

Requirements

We are seeking an experienced MLOps Engineer to join our team in supporting a multi-agent GenAI solution aimed at enhancing the bid and engineering process lifecycle. The ideal candidate will be responsible for overseeing the deployment, automation, monitoring, and optimization of machine learning models within our Azure-based cloud infrastructure, ensuring seamless integration and continuous delivery of AI capabilities.

Requirements:

• Minimum of 3-5 years of experience in MLOps, DevOps, or a related field with a focus on machine learning projects.
• Proven experience in deploying machine learning models at scale within a cloud environment, preferably Azure.
• Proficiency with Azure Machine Learning, Azure Kubernetes Service, and other Azure-based services.
• Experience with tools like Azure DevOps, Jenkins, or GitLab CI for building automation pipelines.
• Strong skills in Docker and Kubernetes for container orchestration.
• Proficient in Python and familiarity with ML libraries such as TensorFlow, PyTorch.
• Experience with Git for source control management.
• Excellent problem-solving skills and the ability to troubleshoot complex issues in machine learning environments.
• Strong communication skills, capable of collaborating effectively with cross-functional teams.
• Detail-oriented with a commitment to delivering high-quality MLOps solutions.

Job responsibilities

• Design and implement MLOps pipelines to automate the training, testing, and deployment of machine learning models.
• Develop and maintain CI/CD pipelines specifically for machine learning projects to ensure robust and scalable operations.
• Utilize Azure services to set up, manage, and scale machine learning infrastructure, including virtual environments and compute resources
• Collaborate with the DevOps team to integrate MLOps practices seamlessly into existing workflows.
• Deploy models into production environments ensuring high availability and performance.
• Implement monitoring solutions to track model performance, reliability, and accuracy over time, utilizing tools like Prometheus and Grafana.
• Automate data preprocessing and model retraining workflows to facilitate continuous learning and improvement.
• Conduct performance optimization on deployed models to enhance efficiency and reduce latency.
• Work closely with data scientists, machine learning engineers, and solution architects to align on best practices in model lifecycle management.
• Establish and document MLOps best practices and maintain comprehensive records of changes and configurations.
• Ensure that all MLOps practices comply with data security and privacy regulations.
• Implement security measures such as data encryption and secure access controls across machine learning workflows.