Key Responsibilities:
* Develop and maintain CI/CD pipelines for ML models and data workflows.
* Automate model training, testing, deployment, and rollback processes.
* Implement monitoring and alerting for model performance and data drift.
* Optimize infrastructure for cost, scalability, and reliability (cloud or hybrid environments).
* Collaborate with data scientists and software engineers to integrate ML models into production.
* Ensure compliance with security, governance, and reproducibility standards.

Experience:
* 5-8 years of experience in software engineering or data engineering, with at least 3+ years in MLOps.
Preferred Qualifications:
* Experience with large-scale ML systems and distributed training.
* Familiarity with GenAI model deployment and optimization.
* Strong problem-solving and debugging skills in production environments

Required Skills:
* Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
* Experience with containerization (Docker) and orchestration (Kubernetes).
* Familiarity with cloud platforms (AWS, Azure, GCP) and ML services.
* Expertise in CI/CD tools (GitHub Actions, Jenkins, Argo).
* Knowledge of feature stores, model registries, and ML observability tools.
* Understanding of data versioning and experiment tracking (MLflow, DVC).

We are seeking an experienced MLOps Engineer to design, implement, and maintain scalable machine learning pipelines and infrastructure. The ideal candidate will bridge the gap between data science and production systems, ensuring robust deployment, monitoring, and lifecycle management of ML models.