about 3 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design and evolve production MLOps capabilities across the full ML lifecycle.
- Build systems for experiment tracking, artifact management, and production readiness.
- Develop reusable platform tooling and engineering standards to improve delivery velocity.
- Build operational infrastructure for LLM and agentic systems.
- Design evaluation and monitoring frameworks for AI systems.
- Build and optimize large-scale training pipelines for heterogeneous data sources.
- Write clean, modular, production-grade Python services and platform libraries.
- Drive engineering quality through automated testing and CI/CD practices.
Requirements
- 5+ years of experience in software engineering, MLOps, or ML platform engineering.
- Significant experience with production ML infrastructure and lifecycle systems.
- Strong Python engineering skills with a focus on production-grade architecture.
- Understanding of the end-to-end ML lifecycle including training and deployment.
- Experience with large-scale data platforms like Databricks or Spark.
- Familiarity with ML platform and MLOps frameworks such as MLflow or Kubeflow.
- Proven ability to design reusable workflow orchestration using tools like Airflow.
- Strong written and verbal communication skills in English.