Position: Senior ML Engineer
Company: emagine
Experience required: 3y
English level: upper

Do you want to push the boundaries of machine learning? And can you create scalable ML-models applicable as services to other components? Then here is an opportunity for you. 

Core skills needed: 
•	TensorFlow
•	PyTorch
•	Python / Java
•	MySQL / MongoDB

**Tweak our framework and automate your testing**

You will join the Data & Analytics team. As our future ML Engineer, you will build, use, and optimize frameworks for automated testing and evaluation of ML models. You will ensure that your ML models are wrapped as services, matured, and re-trained with user inputs on a continuous basis. 

Data & Analytics is developing its own MLOps platforms, both in collaboration with Google on Vertex, and in-house on premise. You will be involved in further maturing these services, ensuring optimal conditions for model training, and serving. Additionally, you will be: 
•	Building micro services for post-processing 
•	Making sure that results from the models create the necessary user value in the frontend by working closely together with the full stack application developers
•	Building near real time data streaming workflows
•	You thrive when solving high-complexity challenges

**You thrive when solving high-complexity challenges**

Solving problems too complex for deterministic reasoning with the use of machine learning, you need to be able to thoroughly analyse problems using data and statistics. With these skills, you make realistic mockups of data to allow you to test things swiftly, finding pragmatic solutions that balances between the theoretical standpoint and what is actually possible.
Additionally, we expect you to have experience with a majority of the following technologies:
•	ML Frameworks: TensorFlow & PyTorch
•	ML model serving: TensorFlow & Torch serving
•	Coding languages: Python & Java
•	Database technologies such as: MySQL & MongoDB
•	Version control: Git 

Working with us, you will get to know these systems as well:
•	Event streaming: Confluent Kafka, K streams
•	Authentication: Open ID Connect 
•	Containerization: Docker
•	Container orchestration: Kubernetes
•	CI/CD Pipelines: Jenkins / Azure DevOps
•	Load balancing: NGINX
•	Installation scripts: Ansible
•	Requirements: Jira
•	Documentation: Confluence
•	Frontend technologies: React JS, Material UI, JavaScript/TypeScript, Redux 
•	Test framework: Jest