InPost

Senior ML Engineer - Fintech Engineering (m/f/n)

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SeniorOn-site·WarszawaFull-time

#459684·Dodano 9 dni temu·1

Źródło: InPost

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Tech Stack / Keywords

Python SQL Spark DatabricksMLOpsSHAPLIMELLM CI/CDFeature Store

Firma i stanowisko

InPost Pay is building its own fintech platform from the ground up as a large-scale, international initiative. The engineering organization in Poland is lean and AI-native, focusing on agentic engineering and AI-driven development for fintech components operating at InPost's scale.

Wymagania

Proven experience building ML systems in production with audit readiness.
Hands-on design and development using AI daily.
Expertise in applied ML at scale covering feature engineering, training, validation, deployment, and monitoring.
Knowledge of model validation methodologies including backtesting, population stability index (PSI), discrimination metrics (Gini/KS), and calibration.
Skilled in MLOps practices: model versioning, CI/CD, champion/challenger and shadow deployment, drift detection, and automated alerting.
Experience in point-in-time-correct feature engineering and feature store consumption.
Familiarity with explainability techniques such as SHAP and LIME, with documentation to support regulatory defensibility.
Fluent in Python and SQL, experienced with Spark/Databricks for large-scale model training and serving.
Experience with advanced agentic tooling, loop and graph engineering including context and tool design, guardrails, eval design with auditable outputs.
Solid working knowledge of large language models (LLMs).
Collaborative, follows shared patterns, and influences team standards.
Fluent in English (B2+).

Bonus points for:

Hands-on model validation or independent review experience.
Knowledge of CCD2 / consumer-credit regulatory requirements.
Experience with Merchant cash advance (MCA) underwriting and merchant-risk data.
Fraud-detection modelling experience.
IFRS 9 provisioning / expected-credit-loss modelling.
Real-time, low-latency model serving.

Obowiązki

Build and ship credit-scoring and merchant-underwriting models end-to-end, including feature engineering, training, validation, deployment, and monitoring.
Engineer point-in-time-correct features for BNPL and MCA decisioning, collaborating with Data Engineering on source contracts and freshness SLAs.
Design and operate the agent loop and graph to ensure reliable, observable, and cost-controlled agentic delivery.
Deploy and operate models using MLOps tooling, including versioning, CI/CD, and champion/challenger or shadow deployment.
Monitor production models for drift, stability, and performance degradation, performing retraining, recalibration, or rollback as needed.
Design explainable and regulatory-defensible models; document features, decisions, and validation evidence for creditworthiness assessment and audit review.
Model Validation track: build and run validation and backtesting harnesses inside the MLOps pipeline; produce audit-ready evidence packs.
Model Lifecycle track: build experiment, evaluation, retrain, and promotion loops on the model registry and serving layer.

Benefity

Opportunity to work on a greenfield, high-stakes project.
Strategic impact on AI-native ML practices at InPost.
Modern tech stack and lean AI-native team with space for experimentation.
Collaborative and fast-moving work environment promoting autonomy and accountability.
Hybrid work model, ideally one day per week in Warsaw or Kraków office.
B2B contract offered.

InPost

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