About Team: The Machine Learning Engineering Team is immersed since over 6 years in on-prem real-time data & event processing. Now a new journey to modularize the tech stack and move to the cloud has started: all while continuing to develop innovative, data-intensive products and solutions for customers and partners.
Your responsibilities:
- Robust and scalable software development by employing best practices and the state-of-the art design patterns on cloud and/or on-prem.
- Ensuring quality code by proper testing (unit, integration, performance, and end-to-end testing), monitoring, and documenting within the team’s agile way of working.
- Collaborate with cross-functional teams, including data scientists, architects, and site reliability engineers to translate the business needs into data-intensive highly scalable machine intelligent solutions.
- Solve complex real-time marketing challenges to reach millions of customers regularly by designing and implementing pipelines for data preprocessing, feature engineering, and machine learning model training. Realtime campaigns include Location-based campaigns, raffles, and real-time (event driven) coupon assignments.
- Stay up to date with the latest advancements in software engineering and machine learning, and actively apply modern techniques and design patterns to improve the existing tech stack.
- Maintaining and operation of the current software products with the mind-set what you build as a team, you also run as team.
Your Profile:
- Proficiency in programming with Java (Springboot preferably)
- Knowledge of data structures, algorithms (incl. optimization algorithms), and software engineering principles.
- Experience with modern real-time data streaming technologies such as Kafka or GCP Pub/Sub.
- Understanding of SQL and noSQL databases
- Proven ability to work effectively in a collaborative team environment with a passion for modern cloud-native software development, learning new technologies, and professional growth.
- NICE TO HAVE:
- Programming experience using Scala and/or Python is a significant asset.
- Experience of deploying machine learning models on any of hyper-scale cloud providers.
- Familiar with concepts and usage of containerization (Kubernetes, Docker).
- Experience working with Cassandra or GCP Firebase.
- Familiarity with cloud computing platforms and distributed systems for training and deploying machine learning models (like: PyTorch, TensorFlow)
- Current Technologies In-Use:
- Architecture & Frameworks: GCP, Spring Boot, Spark, Kafka, Cassandra, ELK, Oracle Languages: Java, Scala and Python Build & Deployment: Gradle, Jenkins, Ansible, Kubernetes/OpenShift, Docker, Helm, Kubeflow Quality Assurance: Test automation & management, Spock, scalatest, Gatling
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