MLOps Engineer

The project you will be joining is centered around creating an advanced advertising ecosystem, integrating hundreds of millions of smart devices across TV, mobile, and desktop. This initiative aims to connect brands with TV audiences by building a sophisticated advertising platform using machine learning algorithms to enhance targeting, personalization, and optimization.

Responsibilities: Design and develop scalable machine learning infrastructure to support high throughput and low latency
Serve ML models to downstream applications, ensuring they are accessible, scalable, and secure
Manage model versions, ensuring the correct version is served and implementing rollback mechanisms if issues arise
Implement monitoring tools to track performance, health, and usage of the platform, addressing issues like concept drift, data drift, and model degradation
Develop, test, deploy, and maintain data and model training pipelines for ML products.
Integrate the serving infrastructure with other systems, ensuring seamless communication and coordination
Optimize the ML serving system, improving efficiency, reliability, and speed, and automating tasks while maintaining high quality standards
Research and experiment with the latest ML serving technologies, creating quick prototypes and proof-of-concepts
Streamline model deployment and enhance the online A/B testing framework
Collaborate with ML engineers to deploy and serve production-grade ML models at scale.
Technical leadership opportunities based on skills and experience.

Requirements: Tech stack:  Python, REST, AWS, Spark, Snowflake, Snowpark, Github Actions, Airflow, Kubernetes, Grafana, Terraform, TensorFlow, PyTorch
Must have:
Degree in Computer Science or related fields.
Minimum 6 years of industry experience
Experience with Infrastructure as Code (Terraform), cloud solutions, and orchestration tools (e.g., AWS, Sagemaker, Airflow)
Familiarity with CI/CD, ETL, big data tools, and mainstream ML libraries (e.g., MapReduce, Spark, Flink, Kafka, Unix/Linux, Docker, Kubernetes, TensorFlow, PyTorch)
Experience with real-time monitoring/alerting components (e.g., Prometheus/Grafana)
Proficiency in Python, Go, or other OOP languages
Experience with distributed cache systems (e.g., Redis/Aerospike)

Offer: Friendly atmosphere focused on teamwork
Comprehensive training and support in developing algorithmic skills
Opportunity to work on multiple projects with the latest technologies
Monthly integration budget
Opportunities to attend local and international conferences
Flexible working hours
Private medical care (including family members)
Multisport card
Life insurance
Lunch card
Various discounts (products, theaters, restaurants)
Opportunity to test new products
Hybrid work model: 3 days from the office per week (Warsaw)
Attractive relocation package