About the Company:

A leading technology organization delivering AI, machine learning, and cloud-native solutions for global enterprises. The company specializes in building scalable AI platforms, MLOps pipelines, and production-grade machine learning systems, enabling organizations to accelerate AI adoption and innovation.

Job Summary:

We are seeking an experienced MLOps Engineer with expertise in MLOps, LLMOps, cloud platforms, and machine learning infrastructure. The ideal candidate will be responsible for deploying, monitoring, and optimizing ML/LLM models in production while building scalable automation pipelines and cloud-native AI platforms.

Key Responsibilities:

- Design, develop, and maintain scalable MLOps and LLMOps pipelines for production AI systems.

- Deploy and manage machine learning and large language models across AWS, Azure, or GCP environments.

- Build and optimize CI/CD pipelines for ML model training, testing, deployment, and monitoring.

- Implement containerized deployments using Docker and Kubernetes.

- Deploy and manage models using TensorFlow Serving, TorchServe, Kubeflow, or MLflow.

- Monitor model performance, latency, drift, and system health using Prometheus and Grafana.

- Develop and maintain data pipelines using Apache Kafka, Apache Airflow, or Apache Beam.

- Optimize AI model performance through distributed computing, model compression, and hardware acceleration (GPUs/TPUs).

- Collaborate with Data Scientists, ML Engineers, and DevOps teams to deliver production-ready AI solutions.

Required Skills:

- MLOps, LLMOps, Python, AWS, Azure, GCP, Docker, Kubernetes, CI/CD, Jenkins, GitLab CI, CircleCI, Terraform, Ansible, TensorFlow, PyTorch, Hugging Face, TensorFlow Serving, TorchServe, Kubeflow, MLflow, Apache Kafka, Apache Airflow, Apache Beam, Prometheus, Grafana, Distributed Systems, Model Deployment, Model Monitoring, GPU/TPU Optimization.

Job Highlights:

- Build and manage enterprise-scale MLOps and LLMOps platforms.

- Deploy and optimize production-ready AI and Large Language Model solutions.

- Work with leading cloud technologies, Kubernetes, and modern ML deployment frameworks.

- Collaborate on cutting-edge AI initiatives involving Generative AI, cloud infrastructure, and scalable machine learning systems.