Machine Learning Engineer

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow. Architected an end-to-end MLOps ecosystem for high-scale predictive platforms, bridging the gap between Data Science R&D and production-grade reliability.

Responsibilities:

• Strategy: Led ML infra roadmap; drove innovation in real-time/batch inference
• Ops: Architected scalable K8s/AWS pipelines & automated MLOps lifecycles
• Leadership: Mentored mid-level engineers; established org-wide Best Practices
• Stakeholder: Translated complex ML concepts into actionable business KPIs

Requirements:

• Strong end-to-end MLOps + cloud stack (AWS, Docker, Kubernetes, Airflow, MLflow) and real-time / streaming experience is essential
• Strong understanding of Machine Learning, with the ability to collaborate deeply with Data Scientists on model deployment and optimization.
• Experience with MLflow for experiment tracking and model management, and Airflow or Dagster for orchestrating end-to-end ML pipelines and workflows.