- Location
- Tempe, Arizona, US
- Workplace
- Remote, Onsite
- Type
- Full-time
- Department
- IT
- Experience
- 5+ years
- Source
- BetterTeam
Description
Location: Tempe, Arizona
Work Arrangement: This is a full-time, on-site position. Remote work is not available for this role.
- Design and build complete AI-enabled applications from architecture through deployment.
- Develop backend services, APIs, data models, and user-facing application components.
- Build responsive front-end experiences using modern frameworks such as React, Vue, or Angular.
- Integrate new applications with existing enterprise systems and shared data sources.
- Translate business and operational requirements into secure, scalable technical solutions.
- Build AI capabilities including RAG-based search, summarization, classification, copilot-style assistants, and multi-step agentic workflows.
- Integrate applications with locally hosted and enterprise-managed LLM environments.
- Design, test, version, and improve production prompt templates.
- Develop evaluation methods and test sets to measure AI output quality and reliability.
- Identify and mitigate hallucination, accuracy, latency, context-window, and other LLM-related risks.
- Develop applications in accordance with established security and data-classification requirements.
- Follow secure coding practices and build applications capable of passing automated security and code-quality checks.
- Write unit and integration tests to support reliable releases.
- Follow appropriate secrets-management and credential-handling practices.
- Maintain clear technical documentation for the applications you develop.
- Work with product, engineering, compliance, healthcare, education, and other stakeholders to translate business needs into working applications.
- Take ownership of assigned applications and features from initial design through testing and release.
- Participate in code reviews, technical design discussions, and Agile/Scrum development processes.
- Support documentation and evidence requirements for applications operating in regulated environments.
- Approximately 5 years of professional software engineering experience, including experience building and shipping production applications.
- At least 2 years of hands-on experience developing AI/LLM-based applications.
- Experience developing across the application stack, including backend services, APIs, databases, and front-end interfaces.
- Strong programming experience with one or more of the following:
- Python (FastAPI or Django)
- Java
- Node.js
- TypeScript
- Experience with a modern front-end framework such as React, Vue, or Angular.
- Experience designing or consuming REST and/or GraphQL APIs.
- Experience working with SQL databases and at least one NoSQL data store.
- Experience with Git-based development workflows, unit testing, and integration testing.
- Hands-on experience building at least one meaningful AI-based application using an LLM in production or in a substantial production-oriented prototype.
- Retrieval-Augmented Generation (RAG)
- Prompt engineering and prompt templating
- AI-powered search
- Summarization or classification applications
- Copilot-style assistants
- Agentic or multi-step AI workflows
- LangChain, LlamaIndex, or similar orchestration frameworks
- Vector databases such as pgvector, Qdrant, or Milvus
- Ollama, vLLM, or similar model-serving environments
- Evaluating LLM output for quality, accuracy, and hallucination risk
- Docker
- GitHub Actions or GitLab CI
- Postman or API testing
- Kubernetes fundamentals
- Kong API Gateway
- OWASP Top 10 and OWASP LLM Top 10
- Healthcare technology or HL7/FHIR APIs
- Government or e-governance applications
- Regulated or sensitive-data environments
- ISO/IEC 27001 or ISO/IEC 42001
- CMMI-aligned software development environments
- Think about the complete application rather than only the AI model or feature.
- Have personally built an AI-enabled application and can explain its architecture, data flow, AI components, and user experience.
- Enjoy solving the practical challenges of LLM applications, including hallucination, latency, evaluation, context limits, and reliability.
- Are comfortable owning an application from initial requirements through development, testing, and release.
- Approach prompt development with the same discipline as software development—testing, versioning, reviewing, and documenting changes.
- Can work with both technical and non-technical stakeholders to turn an idea or business requirement into a practical working application.
- Value secure, well-documented development practices, particularly when working with sensitive or regulated information.
- What problem the application solved
- What you personally built or were responsible for
- What AI model(s), framework(s), or tools you used
- How the AI functionality was incorporated into the overall application
- What you learned from the project or would approach differently today