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Mid-level, popular AI platform role with broad skill requirements increases applicant competition.
Generative AI platform skills are transferable across industries but require specialized GenAI and Azure expertise.
Explicit 5–8 year requirement plus mandatory Azure, LangChain, Kubernetes and GenAI stack makes filters strict.
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Design and implement high-performance APIs (RESTful/GraphQL) to expose AI model functionalities focusing on LLMs and custom ML models.
Develop and maintain data pipelines and vector databases for Retrieval-Augmented Generation (RAG) applications using Azure AI tools and frameworks like LangChain and FastAPI.
Own end-to-end development lifecycle including deployment (Docker, Kubernetes on Azure), monitoring, testing for AI systems, and mentor junior engineers in AI engineering best practices.
Bachelor’s or Master’s degree in Computer Science or related field.
5–8 years of software engineering/backend systems experience; 2–3 years specifically in AI (LLMs, RAG pipelines, API-based AI systems).
Strong hands-on experience with Azure AI ecosystem components including Azure AI Foundry, Azure ML, and AKS.
Proficiency in relevant frameworks and technologies such as LangChain, FastAPI, Kubernetes, Docker, and cloud platform deployment.
Experienced software engineer with a deep focus on backend AI platform development in SaaS/cloud environments, especially Microsoft Azure.
Demonstrates advanced system design skills and operational ownership in deploying and monitoring AI/ML services at scale.
Able to lead and mentor teams on best practices for GenAI and traditional ML integration, emphasizing code quality, reliability, and safety controls.