Machine Learning Ops Engineer in Bucuresti

Since 1998, we've been active in the Human Resources consulting market, providing regional coverage across four key areas of expertise: recruitment and selection, personnel leasing, assessment centers and consultancy. As leaders in Transylvania, we've expanded our reach to embrace a culture of continuous improvement, thereby strengthening our position in the Romanian and also regional market. This commitment underscores our dedication to evolve alongside the dynamic needs of our clients and the ever-changing landscape of the business environment. Our success stems from the professionalism of our services, the multidisciplinary expertise of our consulting team and our ongoing collaboration with those who rely on our consultancy services. Building long-term partnerships with clients across diverse industries such as IT&C, automotive, outsourcing, pharma, banking, FMCG and more, is our primary objective. Our commitment to client orientation, teamwork, flexibility, excellence, dedication and responsibility reflects our aim to bring added value to our services. An MLOps Engineer is responsible for managing the lifecycle of machine learning models, ensuring they are deployed, monitored, and maintained effectively. The MLOps Engineer supports the development, training, and deployment of machine learning models, and key skills involve CI/CD Pipelines, Model Deployment, Monitoring and Maintenance, Automation and Performance Optimization. Skills requirement Must Have: Relevant work experience in ML projects Relevant work experience in technologies and frameworks used in ML, examples are: Apache Airflow, sklearn, MLFlow, TensorFlow Knowledge of MLOps architecture and practices Knowledge of data manipulation and transformation, e.g. SQL Experience working in cloud environment (e.g. GCP)  Programming in Python Experience with monitoring and observability (ELK stack) Familiar with software engineering practices like versioning, testing, documentation, code review Deployment and provisioning automation tools e.g. Docker, Kubernetes, Openshift, CI/CD Nice to Have:  Experience with distributed systems and clusters for both batch as well as streaming data (S3/Spark/Kafka/Flink)  Affinity with Advanced Analytics, Data Science, NLP Hands-on experience building complex data pipelines e.g. ETL  System design and architecture  Bash scripting and Linux systems administration  Programming in a statically typed language, e.g. Scala, Java  Experience with building distributed, large scale and secure applications  Experience with working in an agile/scrum way  Being a committer to Open-Source projects is a strong plus