Head of ML & MLOps Engineering – Fintech

This is a fully remote position, open to applicants in Mali.

📋 Description

• Establish the ML and MLOps function from the ground up.

• Create a cutting-edge ML platform along with the necessary discipline surrounding it.

• Design credit and decision-making models with comprehensive validation, champion/challenger testing, and explainability.

• Engineer production ML serving, monitoring, reproducibility, and retraining processes.

• Implement responsible AI practices, including model risk, bias, and explainability assessments with an independent sign-off gate prior to production deployment.

• Develop agent-first production systems featuring orchestration, guardrails, evaluations, and observability.

• Utilize a point-in-time-correct feature store and governed data.

• Lead the ML & MLOps team starting from the first hire.

• Set standards for model development and validation methodologies to address model risk and regulatory scrutiny.

• Manage the ML platform that supports decision-making services.

• Define responsibilities between feature production and model consumption in collaboration with Data Engineering leadership.

• Oversee compute budget, personnel, and return on investment.

⛳️ Requirements

• Proven experience across the entire ML lifecycle: development, validation, deployment, monitoring, and retraining.

• Background in credit-scoring or underwriting modeling, or similar high-stakes ML applications.

• Expertise in model risk management and responsible AI governance.

• Experience in building and leading a team from inception.

• Proficiency in English (B2+ level).

• Recommended 7+ years in ML and at least 3+ years in a leadership role.

• Bonus: Knowledge of CCD2 and consumer-credit regulations.

• Bonus: Familiarity with DORA/ICT risk.

• Bonus: Understanding of IFRS 9 implications for model outputs.

• Bonus: Experience in fraud-detection ML.

• Bonus: Proficiency with Databricks/Spark.

🏝️ Benefits

• Opportunity to be part of a core leadership team.

• Build systems correctly from the start with no existing legacy ML infrastructure.

• Work at a fast pace within a lean, AI-focused organization.

• Option for employees to work remotely.