ML Engineer

About This Opportunity

We are representing a confidential fintech organization that is scaling rapidly across Europe. Our talent team is seeking an experienced ML Engineer to join their AI division in France (Paris-based or fully remote within France).

This role sits within the Data department, where you'll build and operate machine learning products at the core of the business—from credit scoring models that balance customer acceptance with risk management, to operational intelligence solutions including revenue forecasting and debt collection optimization.

You'll join a lean team of five engineers who own ML products end-to-end, from initial modeling through to production systems that serve and monitor them in live environments.

Responsibilities

• Own ML models across their full lifecycle - build data pipelines, engineer features, train and evaluate models, deploy to production, and monitor performance; select appropriate metrics and guard against leakage, overfitting, and drift
• Run and improve the ML platform - maintain GitOps CI/CD and release processes, monitor serving endpoints, latency and load, define SLOs and alerting to ensure model reliability in production
• Deliver ML-driven business value - collaborate with risk, operations, and product teams to identify and ship ML opportunities; share expertise through code reviews and technical leadership

Technology stack: Python, scikit-learn, SQL, Vertex AI Pipelines (Kubeflow), dbt, BigQuery, PostgreSQL, Argo Workflows, Neo4j, FastAPI, Argo CD, Kubernetes, Terraform, Datadog

What We're Looking For

Must-have qualifications:

• 5+ years building and shipping ML in production environments
• Strong proficiency in Python and SQL
• Solid ML fundamentals: evaluation methodologies, data leakage prevention, handling over/underfitting
• Clean, tested, reviewable code practices
• Familiarity with elements of the tech stack mentioned above
• Hybrid skill set - hands-on with MLOps and infrastructure (data pipelines, monitoring, system design, live prediction/streaming) plus comfort reasoning about latency, scale, and reliability
• Autonomous, analytical, business-driven approach
• Professional English and working proficiency in French (the team's daily working language)

Nice-to-have experience:

• GCP, Docker, or graph databases
• LLMs as development tools and embedded product components
• Credit scoring, buy-now-pay-later, financial services, or fraud detection domains

If you don't meet every requirement but this role excites you, we still encourage you to apply. Our client values potential, curiosity, and growth capacity as much as experience.

How We Work With You

Candidates apply directly through Get A Job.ai. Our recruiting team will conduct an initial screening, and qualified candidates will be submitted to our client for consideration. Please do not attempt to contact the employer directly—all communication flows through our team to ensure a smooth, professional process.

The client's interview process includes: a video call with their talent team, a technical discussion with the hiring manager covering a significant project you've owned, an applied ML and system design session with team members, and a final fit interview with senior leadership.

Pay

Compensation details will be discussed during the screening process based on your experience and qualifications.

Benefits at the client include: competitive 12-month salary, profit-sharing, 100% employer-covered health and disability insurance (including family coverage), sports partnerships or €30/month reimbursement, generous parental leave with 100% salary maintenance, sustainable mobility package (€544.80/year for non-remote contracts), meal vouchers (€10/day, 50% covered), mental health platform access, 25 days PTO plus additional leave for executive contracts, learning and development platform access, and 2 weeks of full remote work possible during summer.

Equal Opportunity

Get A Job.ai is committed to inclusive hiring practices. We welcome applicants from all backgrounds and make employment recommendations based on qualifications, skills, and role requirements.