Lead Engineer (AI/ML - Model Automation)

• Design, develop, and maintain automation pipelines for model training, evaluation, and deployment using Kubernetes and GCP.
• Build and enhance the unified web UI for managing model development, logging, evaluation, and dataset creation.
• Integrate CI/CD pipelines for automated model refresh, versioning, and lifecycle management.
• Develop and manage database-backed systems for storing model metadata, training datasets, and automation manifests.
• Implement and optimize pipeline/template systems for workflow automation and onboarding new models.
• Ensure robust separation and monitoring of research and production environments.
• Develop dashboards for monitoring model refreshes, training jobs, and automation statistics.
• Automate model logging, including source code, parameters, metrics, and datasets for governance and reproducibility.
• Collaborate with data scientists, ML engineers, and platform teams to deliver scalable, reliable solutions.
• Participate in code reviews, architecture discussions, and continuous improvement of the platform

Required Technical Skills

• Proficiency in Python and experience with ML/AI model development (TensorFlow, PyTorch, scikit-learn, MLflow).
• Experience with Kubernetes and Google Cloud Platform (GCP) for orchestration, compute, and storage.
• Hands-on experience with CI/CD tools and automation of ML workflows.
• Strong understanding of database systems for metadata and artifact management.
• Familiarity with web UI development and integration (React, Angular, or similar frameworks).
• Experience with containerization (Docker) and cloud-native deployment patterns.
• Knowledge of model governance, logging, and reproducibility best practices.
• Excellent troubleshooting, debugging, and communication skills.

Preferred Qualifications

• Experience with agentic frameworks (LangChain, CrewAI) and LLM integration.
• Familiarity with large-scale financial/ML platforms.
• Experience with pipeline/template management systems and workflow automation.
• Exposure to model security, vulnerability scanning, and compliance automation.
• Knowledge of monitoring, metrics, and dashboarding tools.