Senior ML Engineer, FinTech (AI-Native) Hybrid

InPost

Warszawa

Hybrid

PLN 260.000 - 340.000

Vollzeit

Vor 3 Tagen
Sei unter den ersten Bewerbenden

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Benefits dieser Stelle

Hybrid work model
B2B contract
Greenfield projects

Zusammenfassung

InPost Pay is hiring Senior ML Engineers to drive the FinTech ML function. You will own agentic model lifecycle, validation, and observability, with emphasis on feature-store-based pipelines for BNPL/MCA decisions.

The role covers end-to-end model development, deployment, monitoring and explainability in a fast-paced, hybrid Poland-based team. You will collaborate across model validation and lifecycle tracks, build auditable evidence, and work with modern ML tooling in a lean, AI-native

Qualifikationen

  • Hands-on ML systems in production with audit readiness.
  • End-to-end lifecycle: feature engineering, training, validation, deployment, monitoring.
  • Experience with validation: backtesting, PSI, Gini/KS, calibration.
  • Proficiency in MLOps: versioning, CI/CD, drift detection, alerting.
  • Feature-store usage and explainability for regulatory defensibility.

Aufgaben

  • Build and ship credit-scoring and merchant-underwriting models end-to-end.
  • Engineer features for BNPL and MCA decisioning with the feature store.
  • Design and operate agent loops and graphs for reliability and cost control.
  • Deploy and operate models with MLOps tooling and CI/CD.
  • Monitor drift and performance; retrain or rollback when needed.

Kenntnisse

Python
SQL
MLOps
Spark/Databricks
LLMs
Feature store

Tools

Databricks
CI/CD tooling
Model registry

Jobbeschreibung

InPost Pay is hiring Senior ML Engineers to drive the FinTech ML function. You will own agentic model lifecycle, validation, and observability, with emphasis on feature-store-based pipelines for BNPL/MCA decisions.

The role covers end-to-end model development, deployment, monitoring and explainability in a fast-paced, hybrid Poland-based team. You will collaborate across model validation and lifecycle tracks, build auditable evidence, and work with modern ML tooling in a lean, AI-native

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