TEKEVER spans several markets and covers a vast set of technological areas and, currently, the company focuses its research activities on the Air Ray class of autonomous systems.Our mission is to bring to our customers the actionable intelligence they need to make the best decisions faster.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: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related fieldExperience: 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 make a difference;Salary Compatible with the level of proven experience. If the above excites you, apply now! Send your CV to ******.
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