Work model: Hybrid (3 days from the office, 2 days remote)

Take ownership of a production Machine Learning Platform as a Senior MLOps Engineer at Starbyte. You will work across cloud infrastructure, CI/CD, Kubernetes, model deployment, and platform engineering while helping the team improve the reliability and scalability of its ML environment.

This role combines hands-on engineering with technical leadership. You will also have the opportunity to shape Starbyte’s growing AI and LLM infrastructure, including GPU hosting and serving larger open source models.

About Starbyte

Starbyte is a technology company and part of WALTER GROUP, an international logistics group operating complex transport networks across Europe.

You will join an established MLOps team in Sarajevo and work closely with Data Science and DataOps colleagues. The current Machine Learning Platform is primarily built on Google Cloud Platform, including Vertex AI, BigQuery, Cloud Storage, and Pub/Sub, with several AI solutions already running in production.

Why should you apply?

Take real ownership of the Machine Learning Platform and influence architecture and engineering decisions
Work with GCP, Kubernetes, Terraform, Python, CI/CD, and modern MLOps tooling
Help shape the next stage of AI and LLM infrastructure, including GPU hosting and model optimization
Combine hands-on engineering with technical leadership, mentoring, and improving engineering standards
Work hybrid with flexible working hours and continuous professional development opportunities

What will your day look like?

You will work on improving the platform that Data Scientists use to develop and deploy machine learning solutions. Alongside hands-on engineering, you will help the team make stronger technical decisions and improve the reliability of production ML systems.

Develop and improve the existing Machine Learning Platform and its reusable ML ecosystem
Work with Python, CI/CD, Docker, Kubernetes, cloud services, and infrastructure as code
Improve model deployment, observability, platform reliability, and production operations
Contribute to AI and LLM infrastructure, including model serving, GPU hosting, and optimization
Guide architecture decisions, mentor colleagues, and support the team with complex technical issues

You are the right candidate if you...

You have strong hands-on experience with MLOps or ML platform engineering
You have practical experience with Python, CI/CD, Docker, and Kubernetes
You have solid cloud experience, ideally with GCP and Vertex AI, while AWS or Azure experience is also relevant
You have taken ownership of architecture, engineering standards, mentoring, or technical solutions
You have experience deploying ML models in production. Experience with Terraform, Kafka, Hugging Face, GPU hosting, fine-tuning, or quantization is a strong plus

If you want to stay hands-on while taking more ownership of an ML platform and helping shape its AI infrastructure, apply or contact aminak@popcornrecruiters.com for more information.