We are seeking a Machine Learning DevOps Engineer with strong software engineering skills to bridge the gap between model development and production deployment. You’ll collaborate with data scientists, backend engineers, and data engineers to design robust ML pipelines, enable CI/CD for ML workflows, and build monitoring systems that ensure performance, compliance, and stability at scale. This is a high-impact role critical to ensuring our AI systems are production-ready, explainable, and secure.

Key Responsibilities

• Develop, maintain, and optimize ML model pipelines using tools like MLflow, Airflow, Kubeflow, or Vertex AI.
• Implement versioning, reproducibility, and deployment automation for models.
• Integrate model validation and testing into CI/CD pipelines (GitHub Actions, Jenkins, etc.).
• Build automated monitoring for model drift, performance degradation, and inference reliability.
• Integrate logging, alerting, and rollback mechanisms for production pipelines.
• Collaborate with software engineers and cloud teams to ensure fault-tolerant deployments (GCP preferred).
• Design, implement, and optimize data storage solutions for ML workflows, working with both relational (SQL) and non-relational (NoSQL) databases.
• Collaborate with data engineers to manage data ingestion, transformation, and database schema design to support scalable fraud detection pipelines.
• Ensure secure, compliant data handling and database governance aligned with financial industry standards.
• Deploy and maintain REST/gRPC APIs for real-time fraud scoring and analytics services.
• Contribute to modular design of analytics engines and microservices for scalability and reliability.

Qualifications Must-Have:

• Develop, maintain, and optimize ML model pipelines using tools like MLflow, Airflow, Kubeflow, or Vertex AI.
• Implement versioning, reproducibility, and deployment automation for models.
• Integrate model validation and testing into CI/CD pipelines (GitHub Actions, Jenkins, etc.).
• Build automated monitoring for model drift, performance degradation, and inference reliability.
• Integrate logging, alerting, and rollback mechanisms for production pipelines.
• Collaborate with software engineers and cloud teams to ensure fault-tolerant deployments (GCP preferred).
• Design, implement, and optimize data storage solutions for ML workflows, working with both relational (SQL) and non-relational (NoSQL) databases.
• Collaborate with data engineers to manage data ingestion, transformation, and database schema design to support scalable fraud detection pipelines.
• Ensure secure, compliant data handling and database governance aligned with financial industry standards.
• Deploy and maintain REST/gRPC APIs for real-time fraud scoring and analytics services.
• Contribute to modular design of analytics engines and microservices for scalability and reliability.

Nice-to-Have:

• Experience with fintech or regulated environments (insurance, banking, AML/KYC).
• Familiarity with explainability (SHAP, LIME), data lineage, and model audits.
• Exposure to GCP (BigQuery, Vertex AI, Dataflow) or other cloud ML platforms.
• Understanding of decision trees or ensemble learning techniques.