Education A Bachelor&aposs or Master's degree in Computer Science, Artificial Intelligence, Applied Mathematics, Physics, or a related technical field. Equivalent hands-on experience in AI/ML engineering, DevOps, or systems architecture may also be considered. Required Experience Experience in developing and deploying machine learning systems using Python, containerization tools like Docker and Podman, and Linux-based operating systems such as Ubuntu or RHEL. Experience with orchestration platforms like Kubernetes or Docker Swarm, and CI/CD tools such as Git, Jenkins, and GitHub Actions. Proficiency in monitoring and logging tools such as ELK Stack, Fluentd, Prometheus, Telegraf, and various data streaming platforms like Apache Kafka, Flink, Storm, and RabbitMQ. Practical knowledge of relational and NoSQL databases such as PostgreSQL, MariaDB, MySQL, MongoDB, Redis, and InfluxDB. Hands-on experience with AI/ML frameworks like TensorFlow, PyTorch, Transformers, Scikit-learn, Ollama, LangChain, and CrewAI. Familiarity with configuration and infrastructure tools including Ansible, Puppet, SaltStack, as well as visualization libraries such as Grafana, Kibana, Matplotlib, and Plotly. Working knowledge of AI model deployment frameworks such as TensorFlow Serving, ONNX Runtime, TorchServe, Nvidia Triton, and API services using FastAPI and Streamlit. Certifications (Preferred) AWS Certified Machine Learning - Specialty Certified Kubernetes Administrator (CKA) TensorFlow Developer Certificate Microsoft Azure AI Engineer Associate Certified MLOps Engineer from recognized training platforms (e.g., Coursera, DataCamp, Udacity) Show more Show less