Company Description: NStarX builds the platform backbone that powers every product pod — including the ML platform, CI/CD/CT pipelines, and infrastructure that make reliable training, deployment, and monitoring of machine learning models possible at scale.

Job Description: Establishes and operates the ML platform, CI/CD/CT pipelines, and infrastructure that enable reliable training, deployment, and monitoring of ML models for the POD.

Key Responsibilities

• Build CI/CD/CT pipelines for automated model training, testing, and deployment.
• Manage ML platform infrastructure (Azure ML, Kubeflow, MLflow, or similar).
• Implement model registry, versioning, and reproducibility practices.
• Monitor model performance, data/model drift, and infrastructure health in production.

Skills and Experience

• 7+ years of experience in MLOps, DevOps, or ML platform engineering roles.
• Hands-on experience building CI/CD/CT pipelines for automated model training, testing, and deployment.
• Proven experience managing ML platform infrastructure such as Azure ML, Kubeflow, MLflow, or similar tooling.
• Experience implementing model registry, versioning, and reproducibility practices.
• Strong Python skills and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Solid understanding of containerization and orchestration (Docker, Kubernetes) for ML workloads.
• Experience with cloud infrastructure and infrastructure-as-code (Azure, Terraform, or similar).
• Strong cross-functional communication skills, partnering closely with data science and platform teams.

Good to Have

• Experience with model monitoring and observability tools (e.g., Evidently, Prometheus, Grafana).
• Familiarity with feature stores and data versioning tools (e.g., Feast, DVC).
• Exposure to LLM/GenAI model serving and fine-tuning pipelines.
• Relevant cloud or Kubernetes certifications (e.g., Azure AI Engineer, CKA).