This role on the platform team will give you the opportunity to work on

• Building out feature computation, storage, monitoring, analysis and serving systems for billions of features required across ShareChat applications every day

• Developing distributed real-time training experiment infrastructure over terabytes of data

• Developing a highly scalable, high-QPS inference service providing low latency performance using a mix of CPU and GPU hardware to most efficiently utilise resources

• Scaling distributed backend services to reliably support high-QPS low latency use cases

• Building core data and model metadata systems powering the end-to-end ML lifecycle

• Advancing the usage of ML monitoring, observability and explainability across the company

To accomplish this, you will make use of cutting-edge open source technologies like Kubernetes, Docker, Golang, Python, Kafka, Spark, Snowflake, Metabase, Tensorflow, PyTorch among others.

You should have,

• 5+ years of industry experience with a solid understanding of engineering and infrastructure best practices

• Strong coding skills with Go, Java or Scala. Familiarity with Python is a plus.

• Hands-on experience using data processing tools like Beam, Spark, Flink or Storm in a cloud environment like GCP or AWS and first-hand knowledge about data management concepts

• Written libraries or tools used by other engineers or researchers.

• A keen drive for code quality, reliability, continuous improvement and monitoring.

• Value for team success over personal success

In addition,

• Experience developing and productionising machine learning models is a plus

• Industry experience building end-to-end Machine Learning Infrastructure a big plus

• Experience with Docker, Kubernetes or Mesos, Spark, Storm or Flink is a plus

• Have a preference for participating in end-to-end product development lifecycle: user research, product discovery, coding, testing, deployment, monitoring, and user feedback.