Head of ML & MLOps Engineering - Fintech Engineering (f/m/n)

Brak informacji o wynagrodzeniu

C-Level / ManagerZdalnie·WarszawaFull-time

#476025·Dodano wczoraj·2

Źródło: InPost

Aplikuj teraz

Tech Stack / Keywords

MLMLOpsfeature storeagentic systemsDatabricks SparkCCD2DORA/ICT riskIFRS 9

Podsumowanie oferty

Lead the ML and MLOps function and build a production platform for credit and decisioning models. You will guide the ML & MLOps team and develop agentic systems, model monitoring and observability, and a point-in-time-correct feature store.

Wymagania

Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining.
Experienced in architecting agentic systems for ML, including orchestration, guardrails, evaluations in CI, human-in-the-loop checkpoints and agent observability.
Experienced in building and leading ML teams in regulated settings.
Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML.
Skilled in model-risk management and responsible-AI governance.
Experienced in 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.
Ensure every model has a business case and measurable monetary outcome.
Build credit and decisioning models with rigorous validation, champion/challenger testing and explainability.
Engineer production model serving, monitoring, reproducibility and retraining.
Build in responsible AI controls, including model-risk, bias and explainability checks, with an independent sign-off gate before production.
Build agent systems with orchestration, guardrails, evaluations and observability.
Build models on governed data using a point-in-time-correct feature store.
Manage the function’s compute budget, headcount and return on investment.

What you'll own:

Set the agentic standard for ML, including agentic model iteration, monitoring and observability bound to the feature store.
Own the reliability, cost and latency of agent loops and graphs in production, and hire against the bar.
Build and lead the ML & MLOps team from the first hire onward.
Set model-development standards and validation methodology that meet model-risk and regulatory scrutiny.
Own the ML platform behind decisioning services.
Define the ownership line between feature production (data engineering) and model consumption with the Head of Data Engineering and the Director.

InPost

72 aktywne oferty

Zobacz wszystkie oferty

Aplikuj teraz