Senior Machine Learning Engineer

Role Summary

As a Senior Machine Learning Engineer, you will serve as a technical leader responsible for bridging the gap between data science research and production-grade software engineering. Your primary mission is to build, deploy, and maintain scalable AI/ML solutions within a banking environment, ensuring all models are secure, performant, and compliant with regulatory standards.

Key Responsibilities

• Model Productionization: Transform R&D prototypes into robust, modular Python microservices and design end-to-end pipelines for feature engineering and model training.
• MLOps & Infrastructure: Architect the ML lifecycle using Google Cloud Platform and Vertex AI, establishing automated CI/CD pipelines and monitoring systems for model drift and latency.
• System Integration: Work with architects to embed ML models into existing digital channels and legacy banking systems using containerized services.
• Governance: Maintain strict adherence to data privacy and security protocols while documenting all architectures for audit purposes.

Requirements & Qualifications

• Education: A degree in Computer Science, Data Science, Engineering, Mathematics, or a related UGC-recognized field.
• Experience: At least 4 years in software or data engineering, with a minimum of 3 years specifically focused on MLOps and model deployment in enterprise settings (Banking, Telco, or Insurance experience is a plus).
• Technical Expertise: Mastery of Python and SQL, with knowledge of ML frameworks like TensorFlow or PyTorch. Proficiency in GCP, containerization (Docker/Kubernetes), and API development is essential.
• Soft Skills: A pragmatic engineering mindset, strong problem-solving abilities for hybrid architectures, and the leadership capacity to mentor junior staff.

How to Apply

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