Principal Architect - AI/ML

Job Summary

We are seeking an experienced Principal Architect - AI/ML to lead the design, implementation, and governance of end-to-end AI/ML solutions across our GenAI powered low-code/AI automation platform. The ideal candidate has a proven track record of building, deploying, and managing scalable AI/ML systems - from data ingestion and feature engineering to model deployment, monitoring, and optimization in production environments.

Responsibilities

• Architect end to end AI/ML solutions from business problem identification through production deployment.
• Drive data exploration, feature engineering, model selection, tuning and validation.
• Productionize ML models through APIs, batch pipelines and real-time inference services.
• Establish and strengthen MLOps practices including model versioning, experiment tracking, model registry, CI/CD, monitoring, drift detection and retraining.
• Design scalable, maintainable ML architectures for enterprise deployments.
• Review ML designs, code and technical approaches.
• Mentor and technically guide AI/ML Engineers and Data Scientists.
• Partner with Product Management to translate business requirements into practical AI/ML capabilities.
• Stay hands-on with prototyping, coding, troubleshooting and technical problem solving.

What will you bring to the team

• 12+ years of experience across software/data engineering and AI/ML, with strong hands-on ML delivery experience.
• Strong expertise in Python, Machine Learning and Statistics.
• Experience with Deep Learning, PyTorch / TensorFlow.
• Strong hands-on experience with scikit-learn, Pandas, NumPy, XGBoost / LightGBM or similar.
• Proven experience building and deploying production ML models.
• Strong understanding of MLOps, model lifecycle management, monitoring and drift detection.
• Experience with REST APIs / model-serving architectures, SQL and enterprise datasets.
• Hands-on experience with Docker and software engineering practices.
• Ability to architect scalable, production-grade ML systems.
• Strong code and design review capabilities.

Good To Have

• Computer Vision | OCR / Document AI | NLP | Time-Series Forecasting | Recommendation Systems | Spark | MLflow | Kubeflow | Kubernetes