Enterprise Software Developer Expert

Allstate Allstate · Insurance · Northbrook, IL

This role involves architecting, designing, and implementing AI and machine learning applications, particularly generative AI and RAG patterns, for the insurance industry. The developer will build and manage these applications in production, focusing on automating and enhancing insurance processes, and ensuring AI-based KPIs are met. The role also includes building traditional microservices and establishing CI/CD pipelines.

What you'd actually do

  1. Architect and design digital products using modern tools, software technologies, frameworks, and systems, including Java and Python Machine Learning, and generative Artificial Intelligence (AI) applications unique to the insurance industry.
  2. Build and configure dev-ops and cloud based tools including Jenkins, Azure, AI Foundry, AI Search, AWS, AWS Bedrock, and Amazon Connect.
  3. Apply a systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software to automate and enhance insurance processes with generative AI and machine learning capabilities, using patterns such as RAG.
  4. Build and maintain traditional, non-AI microservice applications specific to the insurance industry.
  5. Own and manage running applications in production including the successful launch of digital products by achieving specific Key Performance Indicators (KPIs) -- AI based key performance indicators including how precise AI output is compared to the human equivalent and time saved.

Skills

Required

  • Java
  • Python
  • Machine Learning
  • generative Artificial Intelligence (AI)
  • RAG
  • DevOps
  • Cloud computing (Azure, AWS)
  • Jenkins
  • Kubernetes
  • Microservices architecture
  • REST
  • Kafka
  • SQL databases
  • NoSql databases
  • Javascript
  • Typescript
  • React
  • React Native
  • Node.js
  • Git

Nice to have

  • AI Foundry
  • AI Search
  • AWS Bedrock
  • Amazon Connect
  • Spring
  • Spring Boot
  • vector databases

What the JD emphasized

  • architecting, designing, and implementing software applications using RAG, Java, Python, Spring, vector databases, SQL databases, NoSql databases, Javascript, Typescript, React, React Native, Node.js, REST, Kubernetes, containers, AWS, Azure, Jenkins, and Git
  • retrieval augmented generation (RAG) for dynamic insurance knowledge retrieval

Other signals

  • architecting and designing digital products using modern tools, software technologies, frameworks, and systems, including Java and Python Machine Learning, and generative Artificial Intelligence (AI) applications unique to the insurance industry
  • Apply a systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software to automate and enhance insurance processes with generative AI and machine learning capabilities, using patterns such as RAG
  • Own and manage running applications in production including the successful launch of digital products by achieving specific Key Performance Indicators (KPIs) -- AI based key performance indicators including how precise AI output is compared to the human equivalent and time saved
  • Use retrieval augmented generation, client-server architecture, pub-sub architecture (Kafka), microservices architecture, and REST
  • Experience must include (quantitative experience requirements not applicable to this section): Architect and design digital products using modern tools, software technologies, frameworks, and systems, including: Java, Python, Spring, and Spring Boot frameworks for microservice development.
  • Apply a systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software to automate and enhance insurance processes, including AI-powered claims adjudication, underwriting workflows, and risk scoring engines.
  • Participate in product scoping, discovery, and framing, and inceptions by providing technical input to translate user features into system designs for system design using retrieval augmented generation (RAG) for dynamic insurance knowledge retrieval.