Head of ML & MLOps Engineering – Fintech Engineering

Job not on LinkedIn

🔥 56 minutes ago

🌐 Mali, Poland – Remote

🇲🇱 Mali – Remote 🇵🇱 Poland – Remote

⏳ Contract/Temporary

🔴 Lead

🤖 Machine Learning Engineer

👻 Ghost score 14%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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InPost Group

WebsiteLinkedInAll Job Openings

10,000+ employees

🛍️ eCommerce

🚗 Transport

📦 Logistics

eCommerce • Transport • Logistics

InPost Group is Europe's leading e-commerce enablement platform, specializing in out-of-home delivery services. Founded in 1999 by CEO Rafal Brzoska, InPost provides cost-effective and environmentally friendly delivery solutions through a robust network of Automated Parcel Machines (APMs) and courier services. The company also offers pick-up drop-off (PUDO) services, enhancing convenience and efficiency for e-commerce merchants across Europe. InPost is committed to sustainability, reducing carbon emissions significantly in its delivery processes. With operations in countries like Poland, France, Benelux, the UK, and Italy, InPost continues to expand its innovative technology solutions to drive long-term growth in the e-commerce sector.

📋 Description

• Build a state-of-the-art ML platform and the discipline around it • Establish models with business cases and measurable monetary outcomes • Build 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 independent sign-off before production • Build production agent systems with orchestration, guardrails, evaluations and observability • Build on a point-in-time-correct feature store • Run the ML & MLOps function with ownership of compute budget, headcount and return on investment • Run agentic model iteration, monitoring and observability bound to the feature store • Own reliability, cost and latency of production agent loops and graphs • Hire and lead the ML & MLOps team from the first hire • 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

• 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; DORA/ICT risk; IFRS 9 implications for model outputs; fraud-detection ML; Databricks/Spark

🏖️ Benefits

• 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

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