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Generalist title, metro location, and mid-level AI role increase applicant competition.
Specialized RAG, LLM, and vector DB skills reduce cross-industry transferability.
Explicit years requirement and mandatory production AI and vector DB skills increase filtering.
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Build and maintain AI-powered applications using platforms like Microsoft Copilot Studio and large language models, delivering production-ready solutions.
Develop responsive user interfaces with modern frontend frameworks (React, Vue, Angular) to facilitate interaction with AI backends.
Integrate AI features into existing products via REST APIs, ensuring robust error handling, latency management, and responsible AI output validation.
2+ years of software engineering experience including at least 1 year focused on production-grade AI solutions.
Full-stack development proficiency: backend (Python/Node.js) and frontend (React/Angular).
Hands-on experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, Milvus, Azure AI Search).
Familiarity with cloud-native services such as AWS, Azure, or GCP.
Experienced in designing and operationalizing end-to-end AI systems combining backend LLM integrations with polished user interfaces.
Able to translate complex AI technical requirements into user-centric features in collaboration with cross-functional teams.
Demonstrates strong understanding of AI model configurations, efficiency tuning, and maintaining reliable, scalable AI solutions in production environments.