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