Lead Machine learning Engineer

Role: Lead Machine Learning Engineer

Experience: 5+ Years

Location: Chennai

Work Mode: Hybrid

Job Summary:

We are seeking a Machine Learning Engineer to design, develop, and deploy advanced AI/ML solutions, including agentic AI systems and traditional machine learning models, for the mortgage servicing and originations domain. This role involves building scalable, production-ready ML models on Google Cloud Platform, applying classification techniques, model optimization, and tuning, and driving AI-powered automation and decision-making across business processes.

Roles & Responsibilities

• Lead the design, development, training, and deployment of AI/ML models, including traditional ML and agentic AI systems.
• Develop classification, regression, and predictive models using structured and unstructured data.
• Perform model tuning, hyperparameter optimization, feature engineering, and model evaluation to improve accuracy and performance.
• Build and manage scalable data pipelines and ML workflows on Google Cloud Platform.
• Implement, monitor, and optimize AI/ML models for performance, latency, scalability, and reliability.
• Collaborate with cross-functional teams to integrate AI/ML solutions into business applications.
• Analyze large datasets to derive actionable insights and support data-driven decision-making.
• Develop and maintain automated testing, validation, and monitoring frameworks for ML models.
• Ensure model reproducibility, versioning, and lifecycle management in production environments.
• Contribute to MLOps practices, including CI/CD pipelines for ML model deployment.
• Document model architectures, workflows, and ensure adherence to data governance and security standards.
• Stay updated with advancements in machine learning, generative AI, and LLM technologies, applying best practices to enhance solutions.
• Troubleshoot and resolve issues related to model performance, deployment, and data integration.

Required Skills

• Python for ML model development
• Traditional Machine Learning Concepts (classification, regression, clustering)
• Model development, tuning, and optimization
• Generative AI, LLMs, RAG, Prompt Engineering
• MLOps (model deployment, monitoring, CI/CD)
• Data preprocessing, feature engineering, and model evaluation

Qualifications & Experience

• 5-8 years of experience in AI/ML model development, including traditional ML and advanced AI systems
• Strong hands-on experience in building and deploying ML models in production
• Experience in architecting scalable ML solutions
• Knowledge of advanced MLOps, automation, and monitoring frameworks
• Understanding of data governance, security, and compliance
• Ability to mentor junior engineers and provide technical leadership