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Tier-1 brand, metro location, mid-level ML role with broad MLOps requirements increases candidate competition.
ML and MLOps skills are transferable, but automotive CV and infotainment increase domain specificity.
Multiple explicit tech, cloud, and years requirements enforce strict shortlisting.
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Own the complete machine learning lifecycle from data curation through to production releases including model training, optimization, deployment, and monitoring.
Design and implement automated model validation and testing frameworks ensuring model quality across different deployment environments.
Develop scalable MLOps pipelines using technologies such as Docker, Jenkins, Kubernetes, MLflow, and Airflow across AWS and GCP cloud platforms.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electronics, or related engineering discipline.
2-5 years of experience in developing and deploying production machine learning solutions.
Proficient in Python programming with API development experience (Flask/FastAPI).
Experience with CI/CD pipelines (Docker, Jenkins), Kubernetes, Airflow, MLflow, and cloud platforms AWS (EC2, SageMaker, S3) and GCP (Vertex AI, BigQuery, Cloud Storage).
Experienced engineer with strong domain expertise in Computer Vision, Object Detection, and Deep Learning frameworks such as PyTorch or TensorFlow.
Capable of managing end-to-end MLOps workflows and automation across hybrid cloud environments.
Comfortable designing and implementing complex model validation and release quality processes in a production ML environment.