Head of ML & MLOps Engineering, Fintech

InPost Group

. Build a state-of-the-art ML platform and the discipline around it .

Posted 10/22/2026contractRemote • MaliLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in the full ML lifecycle, including development, validation, deployment, and monitoring, while leading and building ML teams in regulated environments. Proficient in model-risk management, responsible AI governance, and architecting agentic systems for ML.

Highest-signal resume keywords

ML Lifecycle ManagementModel-Risk ManagementAgentic Systems ArchitectureTeam Leadership in Regulated SettingsCredit-Scoring Model Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML DevelopmentModel ValidationModel DeploymentModel MonitoringModel RetrainingCredit-Scoring ModellingResponsible AI GovernanceBias ChecksExplainability ChecksHuman-in-the-Loop Checkpoints

Soft Skills

LeadershipCollaborationCommunication

Tools & Technologies

DatabricksSparkFeature StoreCI/CD OrchestrationMonitoring Tools

Certifications & Qualifications

CCD2Consumer-Credit Regulation

Industry Keywords

Model-RiskRegulatory ScrutinyFraud-Detection MLDORA/ICT RiskIFRS 9

Tech Stack

Tools & technologies

Spark

About the role

Key responsibilities & impact

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 production ML serving, monitoring, reproducibility and retraining
Implement responsible AI, model-risk, bias and explainability checks with an independent sign-off gate before production
Build agent-first production systems with orchestration, guardrails, evaluations and observability
Build on a point-in-time-correct feature store
Run the ML function with ownership of compute budget, headcount and return on investment
Own agentic model iteration, monitoring and observability bound to the feature store
Own reliability, cost and latency of agent loops and graphs in production
Hire and lead the ML & MLOps team from the first hire onward
Own model-development standards and validation methodology for model-risk and regulatory scrutiny
Own the ML platform behind decisioning services
Define ownership between feature production and model consumption with Data Engineering and the Director
Work with Data Engineering and Credit Risk partners

Requirements

What you’ll need

Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining
Experienced in architecting agentic systems for ML: orchestration, guardrails, evals in CI, human-in-the-loop checkpoints, 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+)
Bonus: CCD2 and consumer-credit regulation
Bonus: DORA/ICT risk
Bonus: IFRS 9 implications for model outputs
Bonus: fraud-detection ML
Bonus: Databricks/Spark

Benefits

Comp & perks

Seat at the table on a core leadership team
Build it right the first time
No legacy ML estate
Real pace in a lean, AI-native organisation