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Well-known analytics brand and popular mid-level AI role attract substantial applicants.
Core AI engineering skills transfer across industries, but agentic AI specialization and insurance context increase domain specificity.
Explicit years plus mandatory generative AI, LLM, and backend skills make screening strict.
Design, develop, and deploy production-grade AI systems leveraging Generative AI, agentic AI frameworks, and large language models to deliver scalable, reliable business solutions.
Build intelligent workflows including multi-agent coordination, prompt engineering, retrieval-augmented generation (RAG), and contextual memory systems, optimizing for latency, cost, and scalability.
Establish evaluation pipelines for AI system accuracy, safety, and performance while contributing to architecture, best practices, and AI governance to ensure responsible model usage.
2-4 years of professional software development experience with Dotnet, React, or Full-stack technologies.
Minimum 2 years hands-on experience building applications using Generative AI or agentic AI systems.
Strong proficiency in Python backend engineering and experience designing RESTful APIs and scalable services.
Practical experience with LLM APIs, embeddings, vector search, RAG pipelines, and familiarity with agent frameworks and autonomous decision workflows.
Experienced engineer capable of combining experimentation with disciplined engineering to deliver impactful AI-driven products.
Deep understanding of AI workflows involving multi-agent coordination, prompt engineering strategies, and AI governance principles.
Strategic contributor able to influence architecture, promote reusable AI engineering patterns, and maintain reliability and observability across complex AI systems.