ML Engineer (3 yrs)

About The Opportunity

• Design, implement, and maintain end-to-end ML models and pipelines for production use—data ingestion, feature engineering, training, validation, and deployment.
• Develop and optimize model code in Python using PyTorch or TensorFlow; ensure reproducibility and robust evaluation.
• Containerize models and services with Docker and deploy to cloud platforms using Kubernetes or managed services.
• Collaborate with product and data teams to translate requirements into performant ML solutions and measurable success metrics.
• Instrument models with monitoring, logging, and automated tests to detect performance drift and maintain SLA targets.
• Contribute to MLOps best practices: CI/CD for models, versioning, experiment tracking, and reproducible pipelines.
  

Skills & Qualifications

• 3 years of hands-on Machine Learning engineering experience (production ML pipelines and deployments).
• Proficiency in Python for ML development and data processing.
• Practical experience with PyTorch or TensorFlow for model development.
• Experience containerizing applications with Docker and deploying on Kubernetes or cloud-managed clusters.
• Working knowledge of AWS services for storage and compute (S3, EC2, EKS/SageMaker) and SQL for data access.
• Strong skills in model validation, A/B testing, monitoring, and troubleshooting model performance in production.
  

Preferred

• Experience with MLOps tools such as MLflow, Kubeflow, or CI/CD tooling for ML.
• Background in NLP or Computer Vision projects and familiarity with relevant libraries and pipelines.
• Prior work on scalable feature stores, online inference, or real-time prediction systems.
  

• Fully remote, India-based role with flexible hours and asynchronous collaboration.
• Opportunity to work on end-to-end ML product features with ownership and visible business impact.
• Collaborative, learning-oriented engineering culture with mentorship and knowledge-sharing.