About The Opportunity A rapid-scaling AI/ML product company operating at the intersection of enterprise automation and GenAI infrastructure, we build scalable, production-ready ML systems that power intelligent workflows for global clients. Our architecture team owns the end-to-end MLOps stack—from data pipelines and model training to deployment, monitoring, and governance—ensuring AI systems are reliable, observable, and compliant at scale.

Role & Responsibilities

- Architect and implement enterprise-grade MLOps platforms using cloud-native tools (AWS/GCP/Azure) to automate model lifecycle workflows.
- Design and enforce CI/CD pipelines for ML models, including versioning, testing, drift detection, and rollback strategies.
- Lead the integration of feature stores, model registries, and monitoring dashboards (e.g., MLflow, Weights & Biases, SageMaker Model Monitor).
- Define and operationalize ML governance policies—including bias detection, explainability, and compliance with GDPR/ISO standards.




- Collaborate with Data Scientists and DevOps to containerize, orchestrate (Kubernetes), and scale ML inference services.
- Mentor junior engineers and establish best practices for reproducible, scalable, and secure ML infrastructure.

Skills & Qualifications Must-Have

- AWS SageMaker
- MLflow
- Kubernetes
- Docker
- TFX
- Prometheus + Grafana
- CI/CD (GitHub Actions, Jenkins, GitLab CI)
- Python

Preferred

- Argo Workflows
- Sagemaker Pipelines
- Model serving frameworks (Seldon, KServe, TorchServe)

Benefits & Culture Highlights

- 100% remote work with flexible hours and no commute stress.
- Access to modern tech stacks and direct ownership of AI infrastructure decisions.
- Opportunity to shape MLOps standards for next-gen AI products in a fast-growing startup.

Skills: aws,infrastructure,ml

📌 ML+ MLOps Architect (7 yrs) (India)
🏢 Orbion Infotech
📍 India