Senior MLOps Engineer — Fintech Platform, Remote/Hybrid

Jit Team; Kraków; Hybrid; PLN 177,000 - 266,000; Full time

Benefits offered by this job

Flexible hybrid work

International team experience

Impactful fintech project

Large Polish employer

Job summary

Jit Team is seeking a Senior MLOps Engineer for a fintech project in Poland. You will build and deploy modern ML solutions, shaping production ML workflows and tooling.

You will work with Python, Databricks, Airflow, Kubernetes, and MLflow, collaborating across engineering and data teams in a flexible hybrid setting. This role offers impact, growth and exposure to global-scale systems.

Qualifications

• 6+ years in MLOps or related platform engineering.
• 4+ years designing/operating model serving in production.
• Strong Python development.
• Hands-on Databricks experience incl. ML workloads.
• Airflow for workflow orchestration.
• Kubernetes/containerized environments.
• MLflow for experiment tracking and deployment workflows.
• ML lifecycle management in production.
• Experience with A/B testing, shadow deployments, advanced release strategies.
• Streaming platforms and CDC architectures.
• IaC principles and tools.
• Event-driven architectures.
• Cloud cost optimization and FinOps practices.
• Highly available, scalable distributed systems.
• Strong cross-team communication and English fluency.

Responsibilities

• Build and develop modern ML solutions for fintech project.
• Operate production ML infrastructure and pipelines.
• Collaborate with data engineers, software engineers and product teams.
• Ensure scalable, resilient, and cost-efficient ML deployments.

Skills

MLOps

Python

Cross-team collaboration

English communication

Communication skills

Tools

Databricks

Apache Airflow

Kubernetes

MLflow

Terraform/OpenTofu

Job description

Jit Team is seeking a Senior MLOps Engineer for a fintech project in Poland. You will build and deploy modern ML solutions, shaping production ML workflows and tooling.

You will work with Python, Databricks, Airflow, Kubernetes, and MLflow, collaborating across engineering and data teams in a flexible hybrid setting. This role offers impact, growth and exposure to global-scale systems.