- Responsible for understanding the business problems to design high level to low level technical flows to solve them.
- Must have a deep understanding of cloud systems (AWS, Azure, GCP), network architectures (subnets, P2P networks, NSGs, private endpoints), identity managements (Authentication and RBAC) and various services available in cloud systems.
- Must have knowledge in Machine Learning (statistical modeling, forecasting, classification), Deep Learning (ANN, RNN, CNN, Generative Adversarial Networks (GAN), transformers and other generative models and NLP systems). Knowledge on AI libraries like Langchain, LlamaIndex is a plus.
- Understanding on various vector DBs (FAISS , Milvus, Azure AI Search etc.) and associated concepts like HNSW, similarities, embeddings, hybrid searches to effectively design solutions.
- Should understand prompt engineering (Chain of Thought, Tree of Thought, consistency, Graph, React etc.) and designing prompt flows (Promptflow, ML flow etc.)
- Must have problem solving abilities combined with good communication skills.
- Must have an understanding DevOps tools and technologies. Knowledge on Azure DevOps, YAML, Shell scripting, Terraform, pipelines is required.
- Needs to have an understanding on how to fine tune models, quantize models and deployment of the models.
- Needs to have experience in designing composable & modular components
- Should have worked on designing integrations between various cloud based services through APIs keeping modular designs in mind
- An understating on data engineering tools and technologies like Databricks, Data factory and understanding of concepts like big data, data pipelines etc is good to have.
- An understanding of front-end tools and technologies like React, Angular is a plus.
- Having worked in a product-based environment and responsible for designing various components of a product is a plus.
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