Key Responsibilities

- Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring.
- Lead the implementation of containerized ML workloads using Kubernetes.
- Collaborate with data scientists and engineers to productionize ML models.
- Automate model lifecycle management including versioning, rollback, and performance tracking.
- Ensure high availability, security, and compliance of ML systems.
- Develop infrastructure as code using tools like Terraform or Helm.
- Establish and enforce best practices for model governance and reproducibility.

Required Qualifications

- Bachelor's degree in computer science, Engineering, or related field (master's preferred).
- 5–10 years of experience in MLOps, DevOps, or software engineering.
- Extensive experience with Kubernetes and container orchestration.
- Proficiency in Python and Bash scripting.




- Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with cloud platforms (AWS, Azure, GCP).
- Knowledge of CI/CD tools and monitoring systems.

Preferred Qualifications

- Experience with Kubeflow, MLflow, or similar platforms.
- Exposure to data versioning tools like DVC or LakeFS.
- Understanding of model explainability and compliance frameworks.
- Contributions to open-source MLOps projects.
- There will be a BGV process for this requirement including:
- Employment Check
- PCC – Police Clearance Certificate (Criminal Record Check)
- English fluency (all teams work internationally, and English is the standard language).
- Candidate should be your Inhouse Bench resource

📌 MLOps Engineer (India)
🏢 Sparix Global
📍 India