Requirements

Expected technologies

Terraform

AWS

Sagemaker

Airflow MWAA

MapReduce

Spark

Flink

Kafka

Docker

Kubernetes

TensorFlow

PyTorch

Prometheus

Grafana

Python

Go

Redis

Optional technologies

Seldon

Triton

ONNX

TensorRT

Protobuf

FlatBuffers

Cap’n Proto

SQL

Operating system

Linux

Our requirements

• Degree in Computer Science or related fields.
• At least 2 years of proven industry experience in microservices.
• Experience with Infrastructure as Code (Terraform), cloud solutions and orchestration tools (AWS e.g. Sagemaker, Airflow MWAA, Step/Lambda, EC2, EMR).
• Familiarity with CI/CD (e.g.: Github Actions, ArgoCD), ETL, big data tools, mainstream ML frameworks (e.g., MapReduce, Spark, Flink, Kafka, Unix/Linux with shell, Docker, Kubernetes, TensorFlow, PyTorch, etc.) and communication protocols (gRPC, HTTP2.0).
• Experience working with real time monitoring/alerting components (e.g., Prometheus/ Grafana/ AWS Quicksight).
• Experience in Python and Go (preferable).
• Experience with distributed cache systems, e.g., Redis/Aerospike.

Optional

• At least 3 years of industry experience in low latency, high throughput distributed microservices and integration e.g. WS/REST.
• Extensive experience with system architecture design for machine learning.
• Knowledge on testing frameworks for online A/B testing, canary, blue-green deployment.
• Knowledge about ML serving technologies, such as Seldon, Triton, ONNX, ONCL, TensorRT.
• Experience with the advertising industry, recommendation systems or real-time bidding (RTB) ecosystem.
• Knowledge of other OOP languages
• Knowledge of SQL scripting
• Knowledge of serialization protocols (Protobuf, FlatBuffers, Cap’n Proto)

Your responsibilities

• Design and develop highly scalable machine learning infrastructure to support high throughput and low latency.
• Serve ML models to downstream applications, ensuring that they are accessible, scalable, and secure.
• Manage model versions and ensure that the correct version is served to clients. Implement a rollback mechanism in case of issues with the current model version.
• Implement monitoring and observability tools to track the performance, health, and usage of the platform and its components. Monitor the performance of the deployed models, addressing issues such as concept drift, data drift, and model degradation over time. Identify and resolve issues promptly, ensuring that the system remains stable and responsive.
• Develop, test, deploy, and maintain data and model training pipelines to support our ML products
• Integrate the serving infrastructure with other systems, such as data pipelines, monitoring tools, and alerting systems. Ensure seamless communication and coordination among these systems.
• Constantly review and optimize the ML serving system. Strive to improve efficiency, reliability, and speed, looking for opportunities to simplify and automate tasks while maintaining high standards of quality.
• Research the latest machine learning serving technologies (e.g., model compilers, GPU deployment, and inference as a service), and keep up-to-date with industry trends and developments.
• Experiment with new scalable machine learning serving architectures tailored to our environment and create quick prototypes / proof-of-concepts.
• Streamline model deployment, unit testing, integration testing, stress testing and shadow testing.
• Enhance the online A/B testing framework
• Work with ML engineers to deploy and serve production-grade, state-of-the-art machine learning models at scale.
• Depending of your skills and experience you will have a chance to technically lead people