ML+ MLOps Architect (7 yrs)

About The Opportunity
A fast-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

Work arrangement
No