About the Role

The MLOps Engineer builds and operates the infrastructure, pipelines, and tooling that carry AI/ML and GenAI models from development into reliable production systems. Reporting into the Chapter Lead: AI Engineering, the role owns deployment automation, monitoring, and the CI/CD practices that keep models running safely at scale bridging AI Engineering, Data Engineering, and platform/DevOps disciplines.

Key Responsibilities
Build and maintain CI/CD pipelines for deploying ML and GenAI models into production settings.
Implement model monitoring, logging, and alerting to detect performance degradation, drift, or unexpected behavior.
Manage model versioning, experiment tracking, and model registry practices (e.g., MLflow, SageMaker, or equivalent).
Containerize and orchestrate model-serving infrastructure (Docker, Kubernetes) for scalable, low-latency inference.
Automate retraining pipelines and rollback mechanisms to safely recover from model or deployment issues.
Collaborate with AI Engineers and Data Engineers to productionize models and GenAI applications reliably.
Ensure model deployments adhere to data security, governance, and compliance requirements.





Key Skills & Experience
3–6 years in MLOps, DevOps, or ML engineering roles, including hands-on production deployment experience.
Proficiency with MLOps/LLMOps platforms such as MLflow, Kubeflow, or SageMaker.
Hands-on experience with Docker, Kubernetes, and CI/CD tooling for automated, reproducible deployments.
Working knowledge of at least one ML framework (TensorFlow, PyTorch, or Scikit-learn) and one cloud platform (Azure, AWS, or GCP).
Experience with monitoring/observability tooling for ML systems (drift detection, performance dashboards).
Familiarity with LLM-specific serving challenges (GPU resource management, inference optimization) is a plus given the growing GenAI/LLMOps scope of this function.
Relevant certification (AWS Certified Machine Learning – Specialty, Google Qualified ML Engineer, Azure AI Engineer Associate, or CKA) is a plus, not required.

Bachelor's degree in Computer Science, Engineering, or related technical field Role & responsibilities

Preferred candidate profile

📌 Machine Learning Engineer Hyderabad
🏢 SRS Infoway
📍 Hyderabad