Senior Machine Learning Engineer

About the role

You’ll work at the intersection of data science and engineering to build, deploy, and scale machine learning systems. This includes improving ML infrastructure, designing reliable real‑time data pipelines, and ensuring models run efficiently and reliably in production.

Key responsibilities

• Consult with data scientists on training machine learning models.
• Support improvements and additions to the ML infrastructure, including hands‑on data and DevOps engineering.
• Design systems that meet strict throughput and latency requirements.
• Implement non‑functional requirements to guarantee high system reliability.

Required profile

• Prior experience productionising ML systems.
• Prior experience training machine learning models.
• Prior experience or strong interest in the FinTech space.
• Experience with system design focused on performance and efficiency.
• Experience applying software‑engineering rigor to ML (CI/CD, testing, automation).

Required skills

• Advanced Python.
• SQL.
• Terraform (Infrastructure as Code).
• Real‑time/event‑driven systems such as Kafka, Kafkaconnect, Pub/Sub.
• Kubernetes, Docker, and deployment strategies (canary, blue‑green).
• CI/CD tools like CircleCI, Drone, GitHub Actions, ArgoCD.
• Big Data technologies (Spark, Dataflow, Flink).
• MLOps tools (Kubeflow, DVC, MLflow).
• Cloud platforms (GCP, AWS, Azure).