MLOps Engineer

MLOps Engineer

What this role is about?

The MLOps Engineer will play a pivotal role in enhancing our AI capabilities by ensuring the efficient deployment and operationalization of machine learning models. The engineer will focus on maintaining scalable and robust ML systems, ensuring adherence to best practices in model lifecycle management, and collaborating across teams to integrate AI-driven solutions into production environments. This position requires expertise in LLMOps and Generative AI to meet evolving business needs.

What will you do?

• Design, build, and maintain robust and scalable pipelines for training, testing, and deploying machine learning models, ensuring alignment with business needs and production-readiness
• Collaborate with Data Scientists and Data Engineers to optimize workflows and streamline the transition from model development to deployment
• Monitor and maintain the performance of machine learning models in production environments, implementing automation for retraining, versioning, and scaling as needed
• Proactively address system bottlenecks and ensure models are highly available and secure
• Partner with Data Platform and Operations teams to ensure seamless integration of ML solutions into the organization’s broader data ecosystem
• Establish shared best practices for managing data and model lifecycle in compliance with governance policies
• Lead initiatives to operationalize GenAI models and LLMOps workflows, focusing on their scalability and real-time applicability
• Ensure that productionized GenAI solutions address practical business challenges and create measurable value
• Leverage the Microsoft tech stack (e.g., Azure ML, Azure Databricks, and other Azure services) to build and maintain efficient and reliable MLOps solutions
• Ensure alignment with corporate IT standards and guidelines while exploring innovative uses of Azure’s ML capabilities
• Design and implement CI/CD pipelines tailored for ML models to ensure smooth and consistent delivery of production-grade solutions
• Automate infrastructure provisioning, model monitoring, and logging to improve system reliability and performance

What are we looking for?

• University Degree in Data Science, Computer Science, Engineering or a related field Research, Engineering or a related field
• 5+ years of professional experience in deploying and maintaining machine learning systems in production, ideally within a technology-driven or data-intensive environment in multinational organizations
• Passionate about leveraging MLOps and Generative AI technologies to ensure robust, scalable, and efficient ML system operations, with a focus on production-level implementation rather than solely experimentation
• Proven experience collaborating with cross-functional teams, including data scientists, data engineers, and business stakeholders, to bridge the gap between research and production deployment
• Strong expertise in reviewing and optimizing machine learning code to ensure production-readiness, scalability, and adherence to best practices
• Excellent problem-solving skills with the ability to identify system bottlenecks, implement efficient solutions, and manage the lifecycle of ML models, including monitoring, retraining, and performance optimization
• Exceptional communication skills to explain technical implementations, system architectures, and operational plans to diverse audiences, including non-technical stakeholders
• Demonstrated ability to proactively identify operational challenges and design automation workflows, pipelines, and systems that align with business objectives
• Strong interpersonal skills for effective collaboration across technical and operational teams
• Proficiency in Python Programming
• Microsoft Azure ecosystem and Distributed Processing (Databricks)
• Knowledge of common supervised and unsupervised Machine Learning techniques
• Knowledge of Deep Learning methods and Generative AI techniques would be a huge asset
• Fluency in English

What are the next steps – Recruitment Process:

Thank you very much for your interest in the role. You are welcome to apply. We will make sure every candidate will receive a reply within 2 weeks after the application deadline.