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ML Ops Engineer

ML Ops Engineer

Siemens

2 - 5 years

4 - 8 Lacs

bengaluru

Posted:17 hours ago| Platform:

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Skills Required

python devops software engineering aws ml model monitoring kubernetes continuous integration github deploying models scikit-learn ci/cd microsoft azure machine learning gitlab ci docker tensorflow data science grafana gcp jenkins pytorch prometheus

Work Mode

Work from Office

Job Type

Full Time

Job Description

ML Ops Engineer

Key Responsibilities:
Model Deployment & Automation:

Design and implement CI/CD pipelines for ML models.
Automate model training, testing, and deployment workflows.

Infrastructure & Scalability:

Manage cloud-based infrastructure (e.g., AWS, Azure, GCP) for ML workloads.
Optimize resource usage and ensure scalability of ML systems.

Monitoring & Maintenance:

Monitor model performance and data drift in production.
Implement logging, alerting, and rollback mechanisms.

Collaboration & Integration:

Work with data scientists to productionize models.
Integrate ML models into existing applications and services.

Security & Compliance:

Ensure data privacy and compliance with relevant regulations.
Implement secure access controls and audit trails.

Required Skills:

Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience with containerization tools (Docker, Kubernetes).
Familiarity with cloud platforms (AWS, GCP, Azure).
Knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI).
Understanding of model monitoring tools (e.g., Prometheus, Grafana, MLflow).
Strong grasp of software engineering principles and DevOps practices.

Preferred Qualifications:

Bachelors or Masters degree in Computer Science, Data Science, or related field.
Experience with data versioning tools (e.g., DVC).
Familiarity with feature stores and model registries.
Prior experience in deploying models in real-time or batch environments.

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