Requirements:• Expertise in public cloud services, particularly in GCP.• Experience working with VertexAI / Kubeflow Pipelines or other MLOps tools like MLFlow.• Experience with machine learning frameworks (TensorFlow, PyTorch) and libraries (e.g., scikit-learn, HuggingFace).• Knowledge of prompt engineering methods, LangChain, Vector databases and machine learning algorithms.• Knowledge of LLMOps concepts and practices, including model versioning, deployment, monitoring, and efficient resource utilization for large language models.• Experience in Infrastructure and Applied DevOps principles utilizing tools for continuous integration and continuous deployment (CI/CD), and Infrastructure as Code (IaC) like Terraform to automate and improve development and release processes.• Knowledge of containerization technologies such as Docker and Kubernetes to enhance the scalability and efficiency of applications.• Proven experience in engineering machine learning systems at scale.• Strong programming abilities in SQL, Java and Python.• Proven experience with the entire ML lifecycle (processing, training, evaluation, deployment, serving, monitoring).• Familiarity with model performance monitoring, data drift detection, and anomaly detection (hands-on experience preferred).• Strong understanding of IT infrastructure and operations, with the ability to set up monitoring/alerting tools and access controls for both infrastructure and applications.• Have strong communication skills to collaborate effectively with cross-functional teams.• Demonstrate a willingness to adapt to new technologies and methodologies in the ever-evolving AIOps landscape. Must Have:• Overall 4+ years experience in ML Ops.• 2+ years of experience building end-to-end data pipelines.• 2+ years of experience building and deploying ML models in a GCP.• 3+ years of experience working with SQL, Java, Python for data analysis. programming.• Technical degree: Computer Science, software engineering or related• Based in Bangalore and willing to work from the office 3 days a week