Machine Learning Engineer

SRS Infoway; Hyderabad, Bengaluru; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

SRS Infoway is seeking an experienced MLOps Engineer to build and operate the infrastructure, pipelines, and tooling that bring AI/ML and GenAI models into production at scale. You will own deployment automation, monitoring, and CI/CD practices enabling reliable, secure model delivery.

In this role you will work across AI Engineering, Data Engineering, and platform/DevOps, implementing model versioning, registry practices, and scalable serving with Docker/Kubernetes while ensuring governance and

Qualifications

• 3–6 years in MLOps, DevOps, or ML engineering with hands-on production deployment.
• Proficiency with ML/LLMOps platforms such as MLflow, Kubeflow, or SageMaker.
• Hands-on experience with Docker, Kubernetes, and CI/CD tooling for deployments.
• Knowledge of at least one ML framework (TensorFlow, PyTorch, or Scikit-learn) and one cloud platform (Azure, AWS, or GCP).
• Experience with monitoring/observability tools for ML systems (drift detection, dashboards).
• Exposure to LLM-specific serving challenges is a plus.

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.

Skills

MLOps

DevOps

ML engineering

Education

Bachelor's degree in Computer Science/Engineering or related field

Tools

Docker

Kubernetes

CI/CD tooling

MLflow

Kubeflow

SageMaker

Job description

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 Professional 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