mlops developer.

Frameworks: Proficiency with ML lifecycle tools like MLflow, Kubeflow, or DVC for model versioning and tracking.
Backend: Strong Python skills, particularly for creating robust API endpoints and managing microservices.
Security & Monitoring: Experience implementing SSO/MFA for AI services and
setting up real-time monitoring for model performance/drift.

Key Responsibilities

• Model Deployment: Lead the transition of models from Jupyter notebooks into production-ready microservices.
• Infrastructure Automation: Build and manage the infrastructure required to support LLMs and RAG pipelines at scale.
• Observability: Implement logging and alerting systems to detect "model drift" or accuracy drops caused by changes in factory sensor data.
• Security Compliance: Ensure all AI tools and internal chatbots meet enterprise security standards and are integrated with company-wide authentication.
• Scalability: Optimize AI services to handle high-frequency data from multiple production lines simultaneously.

• Experience: 2-5+ years of professional experience in DevOps or MLOps, with a specific focus on deploying and maintaining machine learning models in production.
• Automation & CI/CD: Deep expertise in building automated CI/CD pipelines specifically for ML (using Git, Jenkins, or GitHub Actions) and containerization with Docker.

Nice-to-Haves

• Orchestration: Experience with Kubernetes for managing large-scale GPU workloads.
• Cloud/Hybrid: Experience managing hybrid environments (On-premise servers vs. Azure/AWS).
• Testing: Knowledge of automated testing for AI/ML (Unit tests for model logic).

about the manager/team
You will be reporting to General Manager