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Brand backing, generic software title, mid-level seniority, and metro location raise competition.
AI and RAG-specific skills moderately restrict transferability across industries.
Explicit years, required AI production experience, and multiple mandatory tech stacks increase filter strictness.
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Build and maintain end-to-end AI-powered applications including backend AI pipelines and frontend interfaces using frameworks like React or Angular.
Integrate AI features into existing products via REST APIs ensuring error handling, latency management, and safe AI output validation.
Collaborate cross-functionally with product managers, data teams, and domain experts to translate business requirements into production-ready AI solutions.
At least 2 years of software engineering experience, with minimum 1 year focused on production-grade AI solutions.
Proficiency in full-stack development including backend frameworks (Python/Node.js) and frontend frameworks (React/Angular).
Experience with LLM concepts, RAG pipelines, and vector databases such as Pinecone, Milvus, Azure AI Search, or Cosmos DB.
Understanding of cloud-native services like AWS, Azure, or GCP.
Experienced in developing AI-integrated products combining backend intelligence with user-facing interfaces.
Comfortable operating at the intersection of AI technology, product requirements, and user experience design.
Skilled in handling diverse data formats and optimizing performance and efficiency of AI models within production environments.