Sr ML Engineer

Notice period :Looking for immediate to 15 days or serving notice period candidates are preferred

Shift Timings: 3 PM to 12 AM

Work Mode: WFO (5 days/ In week)

Job Title

Machine Learning Engineer Sr.

Job Summary

The Machine Learning Engineer Sr. is responsible for designing, building, and maintaining data pipelines, integrating model pipelines, and implementing machine learning (ML) algorithms. The role serves as a domain expert in machine learning and contributes to developing machine learning strategy and goals.

JOB REQUIREMENTS

Job Responsibilities

• Design, develop, train, and deploy data pipelines, machine learning, and AI models.
• Build endtoend ML pipelines, including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
• Collaborate with data teams to review model-ready datasets and feature documentation, ensuring completeness and accuracy.
• Implement CI/CD pipelines for ML models and data workflows.
• Develop and maintain cloud-based ML infrastructure (Azure).
• Automate model training, testing, and deployment processes.
• Implement monitoring systems for model performance and data drift.
• Ensure solutions follow best practices for security, compliance, and responsible AI.

Requirements, Education, Experience

• Bachelors degree in Computer Science, Data Engineering or related field, or equivalent experience
• 10+ years of technology experience, preferably in financial services
• 5+ years of ML engineering experience
• Proficient understanding of AI engineering concepts, including LLMs and machine learning
• Experience with AI agent frameworks, LLM integration, or Model Context Protocol (MCP) implementations
• Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools
• Excellent communication and understanding of banking processes
• Proficiency in Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks
• Experience with cloud platforms (Azure, AWS or other) and infrastructure-as-code tools
• Strong understanding of enterprise software architecture patterns and distributed systems design