Description

You will manage the full lifecycle of machine learning solutions, bridging the gap between data science development and production-grade engineering.

Responsibilities

• Develop predictive models using structured and unstructured data to drive fraud reduction and operational efficiency.
• Build and maintain ML pipelines using MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring within cloud environments.
• Implement CI/CD workflows for model versioning, monitoring, and retraining.
• Provision containerized environments and support model scoring via low-latency APIs.

Required Skills

• 5+ years of experience in machine learning or related fields.
• Proficiency in Python and SQL.
• Experience with ML libraries including scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on experience with GCP, AWS, or Azure.
• Practical knowledge of Docker and Kubernetes.
• Experience with MLflow, Kubeflow, or Vertex AI.
• Familiarity with data engineering tools such as Airflow or Spark.
• Understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Preferred Skills

• Experience using AutoML tools like Vertex AI AutoML or H2O Driverless AI.

Key Skills

Education

Any Graduate