MLOps Lead: Build & Scale Production ML Platform

Fetcherr; Warszawa; On-site; PLN 180,000 - 320,000; Full time

Job summary

Fetcherr is seeking an experienced MLOps Team Lead to guide a small team of engineers building an internal ML platform that powers our R&D workflows and production pipelines. You will own end-to-end initiatives and set technical direction while contributing code.

You will maintain ML models and data pipelines across structured and unstructured data at scale, collaborating with R&D and stakeholders, and driving migration from Dask to Ray as part of platform evolution.

Qualifications

• BSc or MSc in Computer Science, Mathematics, or Engineering.
• At least 5 years of commercial Python experience.
• At least 3 years of hands-on MLOps in production.
• Experience leading a team of engineers with ownership of delivery.
• Hands-on ML model lifecycle ownership (training, deployment, monitoring).
• Experience with Dagster or Airflow and cloud providers (GCP/AWS/Azure).
• Experience with distributed computing and Docker/Kubernetes.
• Fluent English written and spoken.

Responsibilities

• Lead, mentor, and grow a team of MLOps engineers, owning delivery and quality.
• Own infrastructure and pipeline initiatives from design to production for the LMM group.
• Stay hands-on: contribute to design and code, review work, set standards.
• Drive milestones in ML model lifecycle and infrastructure ownership.
• Collaborate with R&D and stakeholders to translate research needs into scalable systems.
• Guide migration from Dask to Ray and evolve the platform.

Skills

Python programming

MLOps

Team leadership

ML model lifecycle

Dagster

Airflow

Docker

Kubernetes

Cloud platforms

Distributed computing

English proficiency

Education

BSc/MSc in Computer Science, Mathematics or Engineering

Tools

Dagster

Airflow

Docker

Kubernetes

Spark

Job description

Fetcherr is seeking an experienced MLOps Team Lead to guide a small team of engineers building an internal ML platform that powers our R&D workflows and production pipelines. You will own end-to-end initiatives and set technical direction while contributing code.

You will maintain ML models and data pipelines across structured and unstructured data at scale, collaborating with R&D and stakeholders, and driving migration from Dask to Ray as part of platform evolution.