Machine Learning Engineer

2 days ago


Jakarta, Indonesia Amartha Full time

**About the role**:
In this role, you will get to design and build Amartha's machine learning infrastructure and platform. Your efforts will be critical in ensuring rapid and reliable Machine Learning products and functionality for our consumer app and behind-the-scenes efficiency. In our humble opinion, the coolest part of this role is that your work will directly influence the production quality systems at all stages of the Machine Learning lifecycle in Amartha.

**Responsibilities**:

- Deploy the next generation of Machine Learning systems (such as credit scoring models)
- Design, build, evaluate, ship, and refine products by hands-on Machine Learning development
- Design, build, and maintain a scalable machine learning platform to support multiple ML use cases in real time
- Establish guidelines on infrastructure usage
- Hands-on development on Machine Learning infrastructure and collaborate with Data Scientists to optimize Machine Learning models for high-throughput, low-latency use cases
- Develop and deploy architectures through product engineering and existing model deployment tools

**Requirements**:

- At least 2 years of hands-on experience with large-scale systems in data science / software engineering / machine learning engineering
- Strong background in machine learning deployment.
- Hands-on experience implementing or prototyping machine learning systems at scale in Python, Java, Scala, Go or similar languages.
- Proven track-record building large-scale, high-throughput, low-latency production systems
- Experience with relational databases, non-relational databases (BigQuery, Postgres, Redis), cloud environments, and cloud deployment technology (Terraform, Kubernetes, Helm)
- Experience with TensorFlow, PyTorch, Google Cloud Platform and/or AWS platform is a plus
- Experience with ElasticSearch is a plus
- Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam, Spark, Kafka, MLFlow is a plus
- Knowledge of MLOPS best practices is a plus



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