MLOps Engineer

Are you ready to revolutionise the world with TEKEVER? Join us, the European leader in unmanned technology, where cutting‑edge advancements meet unparalleled innovation. We offer a unique surveillance‑as‑a‑service solution that provides real‑time intelligence, enhancing maritime safety and saving lives. TEKEVER is setting new standards in intelligence services, data and AI technologies. Become part of a dynamic team transforming maritime surveillance and making a significant impact on global safety. At TEKEVER, our mission is to provide limitless support through mission‑oriented game‑changers, delivering the right information at the right time to facilitate critical decisions. If you’re passionate about technology and eager to shape the future, TEKEVER is the place for you Job Overview As an MLOps Engineer, you will be responsible for managing and optimizing the machine learning lifecycle, from model development to deployment and monitoring. You will work closely with data scientists, software engineers and IT operations teams to ensure seamless integration, scalability and reliability of machine learning models in production environments. The ideal candidate will have a strong background in both machine learning and DevOps, with experience in building and maintaining robust MLOps pipelines. What will be your responsibilities Pipeline Development – Design, implement and maintain scalable and efficient machine learning pipelines that automate the process of model training, testing, deployment and monitoring. Model Deployment – Collaborate with data scientists to deploy machine learning models to production environments, ensuring they are scalable, reliable and secure. CI / CD Integration – Develop and maintain continuous integration and continuous deployment (CI / CD) processes for machine learning models, ensuring seamless updates and version control. Infrastructure Management – Set up and manage cloud‑based and on‑premise infrastructure for machine learning workflows, including data storage, computing resources and model serving platforms. Monitoring and Maintenance – Monitor the performance and health of deployed models, implementing automated systems for anomaly detection, logging and alerting to ensure high availability and performance. Collaboration – Work closely with cross‑functional teams, including data scientists, software developers and IT operations, to define requirements and deliver solutions that meet business and technical needs. Security – Implement best practices for data security, model governance and compliance, ensuring that machine learning workflows adhere to industry standards and regulations. Documentation – Maintain comprehensive documentation of MLOps processes, infrastructure and best practices for future reference and reproducibility. Innovation – Stay current with the latest advancements in MLOps tools and technologies, continuously improving and evolving the MLOps processes and infrastructure. Profile and requirements Education – Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. Experience – 3+ years of experience in machine learning, DevOps, or a related field, with specific experience in MLOps. Technical Skills Proficiency in programming languages such as Python, Go, Rust, or a similar language. Experience with machine learning and deep learning frameworks such as TensorFlow, TensorRT, PyTorch, or scikit‑learn. Strong knowledge of DevOps practices, including CI / CD, infrastructure as code (IaC) and containerization (Docker, Kubernetes). Experience with version control systems (e.g., Git) and collaborative development tools. Understanding of data engineering concepts and tools for data preprocessing and ETL. Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack). Experience with relevant tooling such as ClearML for ML lifecycle management. Experience in getting machine learning products to production. Experience with cloud platforms such as AWS, Azure, or Google Cloud, with focus on Google Cloud. Analytical Skills – Excellent analytical and problem‑solving skills with the ability to design innovative solutions to complex problems. Communication – Strong verbal and written communication skills, with the ability to effectively collaborate with technical and non‑technical stakeholders. Attention to Detail – High attention to detail and a commitment to ensuring the accuracy and quality of work. Adaptability – Ability to thrive in a fast‑paced, dynamic environment and manage multiple projects simultaneously. What we have to offer you An excellent work environment and an opportunity to create a real impact in the world. A truly high‑tech, state‑of‑the‑art engineering company with flat structure and no politics. Working with the very latest technologies in Data & AI, including Edge AI, Swarming – both within our software platforms and within our embedded on‑board systems. Flexible work arrangements. Professional development opportunities. Collaborative and inclusive work environment. Salary compatible with the level of proven experience. #J-18808-Ljbffr