Experience- 5+ Years
We are looking for an experienced MLOps / AI Ops Engineer to design, build, and manage enterprise AI/ML platforms. The ideal candidate should have hands-on experience in MLOps, AI Platform Engineering, Feature Stores, Model Registry/Catalog, AI Governance, Kubernetes, Azure, and production AI deployments.
Key Responsibilities
- Design and maintain enterprise AI/ML platforms.
- Build and manage Feature Stores and Model Registry/Catalog.
- Develop scalable MLOps pipelines for model training, deployment, and monitoring.
- Implement AI Governance, security, compliance, and observability.
- Deploy AI/ML workloads using Kubernetes and Docker.
- Build and manage Agentic AI platform infrastructure and AI Agent Catalog.
- Collaborate with Data Scientists, Software Engineers, and DevOps teams.
- Monitor production AI systems and ensure high availability.
Required Skills
- 5+ years of experience in MLOps, AI Platform Engineering, or AI Ops.
- Strong experience with Feature Stores (Feast, Databricks Feature Store, Azure ML Feature Store).
- Experience with Model Registry/Catalog (MLflow, Azure ML Registry, SageMaker Model Registry).
- Hands-on experience with MLOps tools (Kubeflow, MLflow, Airflow, GitHub Actions, Jenkins).
- Experience with Kubernetes, Docker, Python, and REST APIs.
- Knowledge of Azure Cloud (preferred).
- Experience with AI Governance, model monitoring, security, and observability.
- Exposure to LLM platforms and Agentic AI is an added advantage.
- Excellent communication and problem-solving skills.
Preferred Qualifications
- Experience designing enterprise AI platforms.
- Experience supporting production AI/ML workloads.
- Knowledge of CI/CD for machine learning pipelines.
Pay: From ₹800,000.00 per year
Application Question(s):
- How many years of hands-on experience do you have in MLOps or AI Platform Engineering?
- Have you worked on enterprise AI/ML platform architecture?
- Have you implemented or managed a Feature Store in a production environment?
- Which Feature Store(s) have you worked with?
- Have you worked with Model Registry or Model Catalog solutions (e.g., MLflow, Azure ML Registry, SageMaker Model Registry)?
- Which cloud platform have you primarily worked on?
- If selected, how soon can you join (Days)?
- Notice Period, CCTC and ECTC?
Work Location: Remote
