Senior ML Engineer

About us

GetInData | Part of Xebia is a leading data company working for international Clients, delivering innovative projects related to Data, AI, Cloud, Analytics, ML/LLM, and GenAI. The company was founded in 2014 by data engineers and today brings together 120 Data & AI experts. Our Clients are both fast-growing scaleups and large corporations that are industry leaders. In 2022, we joined forces with Xebia Group to broaden our horizons and bring new international opportunities.

What about the projects we work with?
We run a variety of projects in which our sweepmasters can excel. Advanced Analytics, Data Platforms, Streaming Analytics Platforms, Machine Learning Models, Generative AI and more. We like working with top technologies and open-source solutions for Data & AI and ML/AI. In our portfolio, you can find Clients from many industries, e.g., media, e-commerce, retail, fintech, banking, and telcos, such as Truecaller, Spotify, ING, Acast, Volt, Play, and Allegro. You can read some customer stories here.

What else do we do besides working on projects?
We conduct many initiatives like Guilds and Labs and other knowledge-sharing initiatives. We build a community around Data & AI, thanks to our conference Big Data Technology Warsaw Summit, meetup Warsaw Data Tech Talks, Radio Data podcast, and DATA Pill newsletter.

Data & AI projects that we run and the company's philosophy of sharing knowledge and ideas in this field make GetInData | Part of Xebia not only a great place to work but also a place that provides you with a real opportunity to boost your career.

If you want to be up to date with the latest news from us, please follow up on our LinkedIn profile.

About role

ML Engineers share fundamental responsibilities in streamlining machine learning project lifecycles. They are dedicated to designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their responsibilities also include collaborating with stakeholders and platform engineers, leveraging expertise in infrastructure setup, model deployment, monitoring, and proficiency with cloud platforms and data processing.

Responsibilities

• Playing a critical role in developing new algorithms and optimizing existing ones
• Adding significant pieces of functionality to the application, largely based on user
  feedback
• Optimizing ML pipelines
• Taking the initiative and proposing new approaches
• Designing and architecting machine learning workflows/machine learning lifecycle process
• Implementing ML workflows / automating CI/CD pipelines
• Collaborating with Platform Engineers to set the infrastructure required to run MLOps processes efficiently