MLOps Engineer

Synerise is not just another tech company. It is a space where our brilliant team consequently brings technology change to the business world and instead of following known paths, we are creating a new one – a next-generation, fully personalized and AI-driven customer experience.

We passionately support our clients by providing an innovative solution (find out more here) - an all-in-one tool, an ever-evolving data platform, enhanced by AI to accelerate the growth of their business and generate outstanding ROI in more than 30 markets for industry leaders in Retail, Banking, eCommerce, Automotive, Insurance, and Telco, processing more than 150 billion transactions annually in offices in Poland and departments all over the world.

We do this by creating an exceptional culture based on freedom of thought, meritocracy, boldness, empathy and respect for talent with great people on board (meet them here), and a happy office environment focusing on courage and enthusiasm in everything we do. 

Currently, we are looking for a highly motivated and skilled MlOps Engineer. In this role, you will be responsible for integrating our product MONAD  both at customers and as proof of concept. MONAD is an enterprise Python library that allows training private foundation models for behavioral data. The ideal candidate for this position will be experienced in using and integrating cloud services like Azure, GCP and AWS as well as on-premise tools into machine learning pipelines, and be knowledgeable about machine learning. Part of this job might require figuring out how to connect to the data sources on the client side e.g. writing a Python connector to a database. This is a technical position, not a client-facing role. 

What will you do on a daily basis?

• Design, develop and deploy end-to-end ML workflows and pipelines on cloud platforms like AWS, Azure, GCP, etc.,
• integrate MONAD into client’s infrastructure – cloud and on-premise (like Exadata, Teradata, Vertica),
• collaborate with data scientists and software engineers to implement MONAD,
• manage ML infrastructure and tools, and develop solutions for continuous integration and deployment.

What will make us a perfect match?

• Experience in building and deploying ML models in production environments,
• knowledge of at least one of the cloud platforms like AWS, Azure, GCP, etc.,
• experience with containerization tools like Docker and Kubernetes,
• strong coding skills in Python and knowledge of SQL (for integration purposes),
• strong problem-solving skills and attention to detail,
• building and supporting CI/CD pipelines., preferably in GitLab.

What will convince us even more?

• Experience with IaaC tools like Terraform,
• experience with apps for orchestrating workloads on Kubernetes like Apache Airflow or Argo Workflows,
• experience with experiment tracking and monitoring tools like MLFlow, Prometheus, Grafana,
• experience with on-premise databases like Exadata, Teradata, Vertica.

As an AI team member, you will experience:

• Working with an award-winning Machine Learning team to develop a proprietary ML technology,
• constant learning and development opportunities in Machine Learning in a team comprised of both researchers and engineers,
• flat structure with ample ownership opportunities.