Senior MLOPS Engineer

Our client, a leader in Vertical AI SaaS solutions, is a pioneering force in the tech industry. With over 20 years of expertise, they collaborate with some of the world's top firms across sectors such as accounting, consulting, investment banking, legal, private capital, and real assets. Their innovative approach, driven by the principles of "Intelligence Applied," is transforming how businesses operate by leveraging the power of AI and cloud technology. A strategic partner with Microsoft, our client delivers cutting-edge industry cloud solutions that are shaping the future of business operations. The company culture is built around accountability, responsibility, and growth, fostering an inclusive, diverse, and collaborative environment. Since going public in 2021, they have expanded rapidly, growing to over 1,200 employees globally with development centers in locations such as Palo Alto, Charlotte, Belfast, Berlin, and Lisbon.

What You’ll Do:

• Design & Automate: Architect and implement robust MLOps and LLMOps pipelines to automate machine learning model training, testing, deployment, and scaling for maximum efficiency.
• Collaborate & Deploy: Work alongside data scientists and software engineers to ensure models meet operational standards and are ready for deployment.
• Enhance CI/CD: Lead the development and enhancement of continuous integration (CI) and continuous deployment (CD) environments tailored for machine learning systems.
• Improve Visibility: Develop monitoring tools and dashboards that provide real-time insights into system performance and expedite troubleshooting.
• Drive Innovation: Champion a culture of continuous improvement, integrating feedback and lessons learned from each ML deployment to enhance future iterations.
• Lead Resilience & Scalability Initiatives: Take ownership of initiatives that boost the resilience and scalability of ML systems, ensuring they are equipped to handle growing demands.

What You Need to Suceed:

• A Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field.
• 3+ years of experience in MLOps, data engineering, or software development, with a proven focus on deploying scalable machine learning systems in production.
• Strong proficiency in Python and hands-on experience with ML frameworks like TensorFlow, PyTorch, MLFlow, Kubeflow, and more.
• Expertise in containerization (Docker, Kubernetes) and automation tools (Jenkins, GitLab CI) to streamline and scale workflows.
• Experience building and maintaining CI/CD pipelines and working in cloud environments (e.g., AWS, Azure, Google Cloud).
• Excellent troubleshooting and problem-solving abilities to diagnose, debug, and optimize machine learning systems.