MLOps Engineer
Location: Remote
Employment Type: C2H
Experience: 8+ Years
Position Overview
We are looking for a highly skilled MLOps Engineer to build, deploy, monitor, and scale machine learning systems in production. The ideal candidate will bridge the gap between data science and platform engineering by creating reliable ML pipelines, automating workflows, and ensuring high availability of AI/ML services.
The MLOps Engineer will work closely with Data Scientists, Software Engineers, DevOps teams, and Product stakeholders to operationalize machine learning models efficiently and securely.
Key Responsibilities Machine Learning Operations
- Design, build, and maintain scalable ML pipelines for training, testing, deployment, and monitoring.
- Automate end-to-end ML workflows using CI/CD practices.
- Deploy machine learning models into production environments using containerization and orchestration tools.
- Monitor model performance, drift, latency, and system reliability.
Infrastructure & Platform Engineering
- Manage cloud-based ML infrastructure on AWS, Azure, or GCP.
- Implement Infrastructure as Code (IaC) using Terraform or CloudFormation.
- Optimize GPU/compute resource utilization for training and inference workloads.
- Ensure scalability, fault tolerance, and security of ML systems.
Collaboration & Integration
- Collaborate with Data Scientists to productionize ML models.
- Partner with software engineering teams to integrate ML services into applications.
- Support experimentation frameworks and reproducible ML environments.
- Maintain versioning for datasets, models, and pipelines.
Monitoring & Governance
- Implement logging, observability, and alerting systems for ML services.
- Ensure compliance with security, governance, and data privacy standards.
- Establish best practices for model lifecycle management.
Required Skills & Qualifications: Technical Skills
- Strong programming experience in Python.
- Experience with ML frameworks such as:
- TensorFlow
- PyTorch
- Scikit-learn
- Hands-on experience with:
- Docker
- Kubernetes
- MLflow
- Airflow / Kubeflow
- GitHub Actions / Jenkins
- Experience with cloud platforms:
- AWS SageMaker
- Azure ML
- Google Vertex AI
- Knowledge of CI/CD pipelines and DevOps practices.
- Familiarity with REST APIs and microservices architecture.
- Experience with monitoring tools such as Prometheus, Grafana, or ELK stack.
Pay: ₹121,219.77 - ₹152,177.55 per month
Work Location: Remote
