Job Title
MLOps Engineer
Experience
3+ Years
Job Description
We are looking for a skilled and passionate MLOps Engineer to join our growing AI/ML team. The ideal candidate will have hands-on experience in building scalable ML infrastructure, deploying production-grade AI/ML systems, and automating end-to-end machine learning workflows across cloud platforms.
This role involves working with modern MLOps ecosystems including Kubernetes, Kubeflow, CI/CD pipelines, cloud orchestration, and Generative AI applications.
Key Responsibilities
- Design and implement CI/CD/CT pipelines for machine learning workflows using Kubeflow
- Build and manage scalable ML infrastructure on GCP (GKE) and Azure (AKS)
- Develop automated model deployment and monitoring pipelines
- Integrate real-time and batch triggers using GCP Pub/Sub and Azure Event Hubs
- Deploy and manage AI/ML applications in production environments
- Collaborate with Data Scientists and AI Engineers for seamless model lifecycle management
- Implement model quality checks, drift detection, and automated retraining workflows
- Ensure scalability, reliability, and performance optimization of ML systems
- Maintain technical documentation and deployment standards
- Troubleshoot production-level ML infrastructure issues
Preferred Candidate Profile
- 3+ years of experience in AI/ML or MLOps Engineering
- Strong understanding of machine learning lifecycle management
- Experience deploying AI/ML systems in production
- Hands-on experience with cloud-native architectures
- Strong debugging and problem-solving skills
- Good communication and documentation abilities
- Agile mindset and collaborative approach
Pay: ₹321,219.77 - ₹1,212,177.55 per year
Application Question(s):
- Do you have hands-on experience with Kubernetes and Kubeflow in production environments?
- Which cloud platforms have you worked on for ML deployments?
(AWS / Azure / GCP)
- Have you implemented CI/CD or Continuous Training (CT) pipelines for machine learning workflows?
Experience:
- MLOps or AI/ML Engineering: 3 years (Required)
Work Location: In person
