
Lead Engineer
2 weeks ago
Overview:
Join the Prodapt team in building the Model Automation Platform—a unified, cloud-native system that streamlines and automates the entire model development lifecycle. You will help design, develop, and optimize a scalable platform that enables rapid onboarding, training, evaluation, and governance of machine learning models across business units.
Responsibilities:
- 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
Requirements:
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.
- 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.
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