About the job
As a Machine Learning Engineer at BigData Republic, you will be a hands-on expert responsible for deploying machine learning solutions into production. You will not only build models but also guide clients on the best strategies to achieve sustainable business value. Do you see yourself in this role now or in the future? If so, we would love to meet you.
At BigData Republic, we reject standard role descriptions, which is part of our strength. We call this authenticity, and we hope you bring a similar approach. Whether you are a data scientist passionate about engineering, a data engineer with a strong interest in AI and LLMs, a software engineer with deep knowledge in deep learning, or a cloud engineer fascinated by computer vision, your enthusiasm for new technology and belief in collective intelligence will help us build something valuable together.
The role of Machine Learning Engineering sits at the intersection of MLOps, software engineering, and cloud engineering. You will collaborate with multidisciplinary teams to create end-to-end machine learning solutions for clients, from concept to production. This can range from relatively simple models with direct business impact to advanced techniques such as causal inference, reinforcement learning, and deep learning.
We approach ML solutions as full-stack software projects. The technologies we frequently use include Python, Airflow, Spark, Kafka, Docker, TensorFlow, PyTorch, Kubernetes, and Terraform across major cloud platforms as well as private cloud environments. Production is key: scalable deployment, model interfaces, and monitoring are integral parts of your role.
You will tackle complex challenges for large organizations across various sectors while being part of a small, informal, and close-knit team of experienced MLOps and Data Engineers (around 10 colleagues). We know each other well, actively share knowledge, and challenge one another to keep growing. Most importantly, you can truly be yourself here.
