The candidate should have proven expertise in building scalable platforms that are customer facing and have expertise in evangelizing the platform with customers and with internal stakeholders Expert level knowledge of Cloud Computing including aspects of VPC Network Design, Shared Responsibility Matrix, Cloud databases, No SQL Databases, Data Pipelines on the cloud, VM and VM orchestration, Serverless frameworks. This should be across all 3 major cloud providers (AWS, Azure, GCP), preferably at least in 2 of the 3 Public Clouds Expert level knowledge in Data Ingestion paradigms & use of different types of databases like OLTP, OLAP for specific purposes Hands-on experience with Apache Spark, Apache Flink, Kafka, Kinesis, Pub/Sub, Databricks, Apache Airflow, Apache Iceberg, and Presto. Expertise in designing ML Pipelines for experiment management, model management, feature management, model retraining, A/B testing of models and design of APIs for model inferencing at scale. Proven expertise with Kube Flow, SageMaker/Vertex AI/Azure AI. SME in LLM Serving paradigms, deep knowledge of GPU architectures, distributed training and serving of large language models. Expertise in Model and Data parallel training, expertise with frameworks like DeepSpeed and service frameworks like vLLM etc. Proven expertise in Model finetuning and model optimization techniques to achieve better latencies, better accuracies in results. Be an expert in reducing training and resource requirements of finetuning of LLM and LVM models. Have a wide knowledge of different LLM models and have an opinion on aspects of applicability of each model based the usecases. Proven expertise of having worked on specific customer usecases and having seen delivery of a solution end to end from engineering to production. Proven expertise in DevOps and LLMOps, knowledge of Kubernetes, Docker and container orchestration, and deep knowledge of LLM Orchestration frameworks like Flowise, Langflow, Langgraph.

Experience:

14-18 years