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MLOps Engineer
3 miesięcy temu
We are looking for a MLOps Engineer for our client. Working in a hybrid model: 3 days from the office per week.
Role and 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 (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
Technologies in use
Python
REST
AWS
Spark
Snowflake
Snowpark
Github Actions
Airflow
Kubernetes
Grafana
Terraform
TensorFlow
PyTorch
Skills and Qualifications
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 (e.g.: AWS, Sagemaker, Airflow, AWS Step/Lambda).
Familiarity with CI/CD (e.g.: Github Actions), ETL, big data tools, and mainstream ML libraries (e.g., MapReduce, Spark, Flink, Kafka, Unix/Linux with shell, Docker, Kubernetes, TensorFlow, PyTorch, Spark ML, etc.).
Experience working with real time monitoring/alerting components (e.g., Prometheus/ Grafana).
Experience in Python, Go or other OOP languages.
Experience with distributed cache systems, e.g., Redis/Aerospike.
Nice to have
At least 5 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.
