Senior Machine Learning Engineer

Join Attain, a leading B2C financial services company, as a Senior Machine Learning Engineer. In this hands-on role, you will design, build, and operate the pipelines, platforms, and tooling that take models from experiment to reliable production service. You will work on high-impact predictive models and collaborate with data scientists, analysts, platform engineers, product managers, and business stakeholders. The ideal candidate has strong software and platform engineering fundamentals, practical MLOps experience, and a track record of replacing manual workflows with automation.

Missions

• Concevoir, construire et exploiter les pipelines, les plateformes et les outils qui permettent de passer des modèles de l'expérimentation à un service de production fiable.
• Construire, déployer et exploiter les systèmes ML de production au cœur de notre produit EWA, en mettant l'accent sur la fiabilité, la performance et l'exécution rapide.
• Automatiser les étapes manuelles et répétitives du cycle de vie ML afin que l'équipe puisse avancer plus rapidement sans sacrifier la fiabilité.

Profil recherché

- The ideal candidate combines strong software and platform engineering fundamentals with practical MLOps experience building and operating production ML systems from scratch, and treats modern AI tooling as a first-class part of how the work gets done-directing coding agents to write, test, and ship infrastructure code, with the judgment to know when to verify their work

- Experience with distributed computing and GPU-accelerated workloads (e.g., Spark, Ray, Dask, or distributed training/inference), including scaling data and model pipelines across clusters

- Experience with model versioning, reproducibility, and safe progressive rollout (shadow, canary, champion-challenger) of models in production

- Experience building the infrastructure behind high-impact applied ML use cases such as credit decisioning, risk modeling, fraud, churn, or consumer behavior modeling

- Experience with cloud computing services or platforms; GCP preferred

- Strong SQL skills and experience with cloud data warehouses and operational databases (e.g., BigQuery, Spanner), including working with large, messy, real-world datasets

- Experience with observability tools such as Prometheus, Grafana, or Datadog

- Strongly preferred: degree in STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field

- 5+ years of direct experience as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist or similar role building and operating production ML systems

- Familiarity with model explainability, auditability, and the compliance considerations of regulated decisioning (a plus for credit/fintech contexts)

- A track record of replacing manual, repetitive ML workflows with durable automation

- Willingness to roll up your sleeves and wear multiple hats across engineering, infrastructure, and ML execution based on business needs

- Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences

- Strong software and platform engineering fundamentals

- Hands-on MLOps experience: pipelines, CI/CD for ML, containerization (Docker), orchestration (Kubernetes), infrastructure-as-code (e.g., Terraform), and workflow schedulers (e.g., Airflow)

- Strong Python coding skills, with the ability to build pipelines, services, and production-quality tooling from scratch; experience with a systems or backend language such as Go or Rust is a plus

- Demonstrated fluency directing AI coding agents (e.g., Claude Code, Cursor, or similar) to build, operate, and debug real ML systems-with experienced judgment on verifying their work

- Experience building low-latency online model serving (e.g., gRPC/microservices, ideally with a service mesh such as Istio) for real-time decisioning

- Demonstrated ability to apply critical thinking, abstract reasoning, and sound engineering judgment to complex, ambiguous technical and business problems

- Strong expertise deploying, serving, monitoring, and operating ML models in production-including feature engineering systems, training/serving parity, retraining, and model performance diagnostics

- We encourage you to apply, even if your experience doesn’t match every detail on the job description. If we don’t see something that immediately fits, we will keep your resume on file for future opportunities