Roles and Responsibilities

• Manage and enhance CI/CD pipelines using GitHub Actions, Flux, and Kustomize for continuous deployment.
• Design and implement cloud-native MLOps solutions on AWS.
• Support containerization and deployment of ML/LLM models using Docker, Kubernetes, and VLLM.
• Collaborate with cross-functional teams (Data Scientists, Engineers, Architects) to understand requirements and improve infrastructure reliability.
• Build and maintain scalable tools and services to support ML model training, evaluation, and inference.
• Document processes, deployment procedures, and best practices clearly and concisely.
• Ensure the platform’s reliability, scalability, and performance for ML workloads.
• Support the development and automation of MLOps pipelines and workflows using tools like Kubeflow, MLflow, Airflow, and Argo.
• Implement and monitor security, compliance, and operational standards for ML services in production.

Required Qualifications

• 8+ years of experience in SRE, DevOps, or ML Ops roles with a strong focus on machine learning infrastructure.
• Strong experience with Kubernetes, Docker, and cloud platforms (AWS preferred).
• Proficient in Python, with experience in scripting and automation.
• Experience working with MongoDB and Apache Solr.
• Strong background in Linux system administration.
• Familiarity with ML and LLM concepts, training, and deployment.
• Hands-on experience with CI/CD pipelines, configuration management, and Infrastructure-as-Code.
• Experience with cloud-native development, container orchestration, and cloud networking.
• Strong understanding of API integration, cloud-based services, and microservices architecture.
• Familiarity with open-source tools and frameworks for MLOps such as Kubeflow, MLflow, Airflow, DataRobot, or Argo.
• Excellent communication skills and the ability to work effectively in a collaborative team environment.

Preferred Qualifications

• Prior experience supporting large-scale ML/LLM systems in production environments.
• Deep understanding of observability and monitoring tools (e.g., Prometheus, Grafana).
• Experience with model versioning, reproducibility, and A/B testing in ML systems.
• Knowledge of security best practices in ML/AI workloads on cloud.
• Exposure to benchmarking, testing, and performance tuning for ML platforms.

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