Data Science & ML Ops Engineer

We are seeking a hybrid Data Science & ML Ops Engineer to drive the full lifecycle of machine learning solutions from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops engineering.

Key Responsibilities:

• Build predictive models using structured/unstructured data across multiple business lines.

• Utilize AutoML tools (Vertex AI AutoML, H2O Driverless AI) for rapid, low-code/no-code model development.

• Develop and maintain ML pipelines using MLflow, Kubeflow, or Vertex AI.

• Automate training, testing, deployment, and monitoring of models in cloud environments (Google Cloud Platform, AWS, Azure).

• Implement CI/CD workflows for model lifecycle management, including retraining and monitoring.

• Ensure compliance with model governance frameworks (MRM, documentation, explainability).

• Deploy models into containerized environments (Docker, Kubernetes) with low-latency APIs.

• Collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders.

Qualifications (Must-Have):

• Strong proficiency in Python and SQL.

• Hands-on experience with ML libraries: scikit-learn, XGBoost, TensorFlow, PyTorch.

• Experience with ML Ops tools & pipelines (MLflow, Kubeflow, Vertex AI).

• Strong exposure to cloud platforms (Google Cloud Platform, AWS, Azure).

• Knowledge of Docker & Kubernetes for model deployment.

• Background in CI/CD workflows for ML lifecycle.

• Excellent communication skills.

Preferred Skills (Nice-to-Have):

• AutoML tools (Vertex AI AutoML, H2O Driverless AI).

• Data engineering tools (Airflow, Spark).

• Experience with fraud detection, customer analytics, or operational efficiency models.