AI/ML Engineer - MLOps/SRE
INR 1800K-3000K (estimate) Mid-level Full Time
Tasks
- Build production ML platform
- Create documentation templates and internal best practices
- Create standardized ML lifecycle workflows
- Define SLIs and SLOs for ML services
- Deploy ML models using containers and orchestration
- Design model registry and artifact storage
- Harden inference services for low latency and high throughput
- Implement CI/CD workflows for ML pipelines
- Implement automated feedback loops for retraining and rollback
- Implement observability for infrastructure and model behavior
- Implement training deployment monitoring pipelines
- Integrate secrets management and RBAC
- Monitor model quality and data health
- Perform vulnerability scanning and network controls
- Run incident response and post incident reviews
- Support compliance and governance for model usage
- Translate business requirements into scalable ML solutions
Perks/Benefits
Skills/Tech-stack
Airflow | Alerting | Artifact storage | Audit Logging | Autoscaling | Azure Machine Learning | Batch inference | Bias monitoring | CI/CD | Caching | Containerization | DVC | Dashboards | Data Drift | Data Quality | Data Validation | Dataset versioning | Distributed Systems | Encryption | Feast | Feature Engineering | Feature Store | GRPC | Great Expectations | Incident Management | Infrastructure as Code | Kubeflow | Kubernetes | Load Balancing | MLOps | MLflow | Machine Learning | Metadata lineage | Metrics | Model Evaluation | Model Monitoring | Model Registry | Model Serving | Monitoring | Network controls | Observability | PyTorch | Python | Queueing | RBAC | REST | SLI | SLO | Scikit-learn | Streaming inference | Structured Logging | TensorFlow | Tracing | Vertex AI | Vulnerability scanning | “as-code”
Education
Roles
AI | AI/ML Engineer | Engineer | ML Engineer | MLOps Engineer | Reliability Engineer | Site Reliability Engineer
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