InPost

Head of ML & MLOps Engineering - Fintech Engineering

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C-Level / ManagerZdalnie·WarszawaFull-time

#464377·Dodano 14 dni temu·4

Źródło: InPost

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Tech Stack / Keywords

MLMLOpsAIfeature storeDatabricks SparkCCD2DORA/ICT riskIFRS 9

Podsumowanie oferty

Build and lead the ML and MLOps function and a production ML platform for credit and decisioning models. The role covers agentic ML systems, model governance and a point-in-time-correct feature store.

Wymagania

Lead with an interest in data and building systems around it.
Be hands-on when needed, especially with AI, and understand the model, pipeline and serving layer.
Experience across the full ML lifecycle: development, validation, deployment, monitoring and retraining.
Experience architecting agentic systems for ML, including orchestration, guardrails, evaluations in CI, human-in-the-loop checkpoints and agent observability.
Experience building and leading ML teams in regulated settings.
Experience in credit-scoring or underwriting modelling, or comparable high-stakes ML.
Skills in model-risk management and responsible-AI governance.
Experience building and leading a team from zero.
Fluent in English (B2+).

Nice to have:

CCD2 and consumer-credit regulation.
DORA/ICT risk.
IFRS 9 implications for model outputs.
Fraud-detection ML.
Databricks/Spark.

Obowiązki

The mission:

Build a state-of-the-art ML platform and the discipline around it.
Develop credit and decisioning models with rigorous validation, champion/challenger testing and explainability.
Engineer model serving, monitoring, reproducibility and retraining for a production ML platform.
Build in responsible AI, including model-risk, bias and explainability checks and an independent sign-off gate before production.
Build agent-first systems with orchestration, guardrails, evaluations and observability.
Build on a point-in-time-correct feature store.
Run the function with responsibility for compute budget, headcount and return on investment.

What you'll own:

The ML and MLOps team, from the first hire onward.
Agentic model iteration, monitoring and observability bound to the feature store.
Reliability, cost and latency of agent loops and graphs in production.
Model-development standards and validation methodology for model-risk and regulatory scrutiny.
The ML platform behind decisioning services.
The ownership line between feature production and model consumption, together with Data Engineering leadership.

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