What are you going to do?

At BigData Republic, Machine Learning Engineering is situated at the intersection of Machine Learning, MLOps, Software Engineering, Data Engineering, and Cloud Engineering.

You work in multidisciplinary teams at clients on end-to-end machine learning solutions: from problem definition and architecture to development, deployment, monitoring, and optimization.

You;

• analyzes complex business and technical issues and translates them into robust ML solutions;
• advises clients and stakeholders on machine learning, MLOps, architecture, and technical choices;
• develops and productionalizes machine learning models;
• designs scalable ML and MLOps architectures;
• builds production-grade software around machine learning solutions;
• works on deployment, CI/CD, model serving, monitoring and observability;
• collaborates with Data Scientists, Data Engineers, Software Engineers, and Cloud Engineers;
• helps clients and colleagues reach a higher level in terms of content;
• Actively contributes to knowledge sharing and thought leadership within BigData Republic.

The technical challenges range from machine learning models with direct business impact to applications involving deep learning, generative AI, Large Language Models (LLMs), predictive modeling, causal inference, and reinforcement learning.

At BigData Republic, we do not view a machine learning model as the end product. A model only delivers value when it runs reliably, scalably, and manageably in production. Therefore, we approach ML solutions as fully-fledged software products.

Technologies we work with frequently include:

• Python
• Airflow
• Apache Spark
• Kafka
• Docker
• Tensorflow
• Pytorch
• Kubernetes
• Terraform
• AWS/Azur/GCP and private cloud environments
• 
  
•