MLOps Specialist

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. .