We are looking for a Data DevOps/MLops developer to join our small Data Science group.
Essential Duties and Responsibilities:
Be responsible for large scale production database deployments and replication providing researchers, tools and algorithms near real-time access and automation.
Be the center of knowledge and help the research team implement ideas and algorithms.
Develop various CI/CD solutions for different kinds of projects.
Use Native Cloud stack and other modern platforms for maximum automation and observability. Partial technical stack: Linux (Ubuntu, CentOS), Databases (MySQL, Postgres, NoSQL, Redis), GCP, AWS, Kubernetes (K8s), docker, Apache Airflow, Grafana, JupyterHub, React, Flask, Nginx, Python (Pandas), git.
Work with internal and external teams to provide accurate and efficient data pipelines, APIs, design and develop from scratch frontend and backend.
Work with large and complex data sets, across many systems, and be involved in building machine learning models and analysis tools.
Work closely with various disciplines researchers on how to make gold out of their data and implement it.
Use your creativity to resolve complex and high scale data related issues, and develop solutions to recurring problems.
The ideal candidate will be a talented Python programmer, with strong Linux DevOps and database experience, that can handle K8s clusters, dockers, linux services, Nginx reverse proxies, a wide array of programming challenges, from hardware sensor db and web integrations (Flask, React) to constructing Apache Airflow data workloads to the inner workings of our barcodes tracker.
To perform the job successfully, an individual should demonstrate the following competencies:
2+ years of Linux DevOps experience, including K8s and Bash
Excellent Python programming and SQL skills
Cloud experience, AWS/GCP, orchestration tools and/or CLI automation
Configuration management and monitoring experience – a plus
Chemistry, Physics and Electro-chemistry knowledge – great advantage