Description

Must-Have Skills (Kubeflow Mandatory):

Programming: Strong Python skills + experience with ML libraries (TensorFlow, PyTorch, scikit-learn)
ML & Data Science: Solid understanding of ML concepts, modeling, and evaluation metrics
Containerization & Orchestration: Hands-on experience with Docker and Kubernetes for ML deployment
CI/CD: Experience creating automated pipelines using Jenkins, GitLab CI, GitHub Actions, etc.
Cloud Platforms: Familiarity with AWS, GCP, or Azure
ML Workflow Automation: Expertise with Kubeflow (mandatory), Airflow, or MLflow
Model Monitoring: Knowledge of tools like Prometheus, Grafana, Seldon, Evidently AI
Version Control: Proficiency with Git for code and model versioning

Good-to-Have Skills:

IAC: Terraform, CloudFormation
Feature Stores & Data Versioning: Feast, DVC
GenAI: Understanding LLM deployment & architecture
Data Engineering: Experience with Spark, Kafka
Serverless & APIs: Familiarity with serverless ML serving
Security & Compliance: ML system security best practices
Experimentation: A/B testing & experimentation frameworks
Distributed Training & Tuning: Large-scale training and HPO
Specialized ML Domains: NLP, Computer Vision, etc.
Software Engineering: Testing, code reviews, best practices

Key Skills

Education

Any Graduate