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 environments.

- 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 Skilled 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 (Bengaluru)
🏢 SRS Infoway
📍 Bengaluru