Software Engineer, AI Solutions

Cardinal Group Career•Denver, CO

18h•Onsite

About The Position

The Software Engineer, Applied AI designs, builds, and supports production software and AI solutions that address business problems across Cardinal. The role spans the full software development lifecycle and owns solutions end-to-end, including web applications, APIs, integrations, automated workflows, and AI agents. It uses agentic development tools such as Claude Code and GitHub Copilot as part of everyday work, while applying computer science fundamentals and engineering judgment to verify that AI-generated work is secure, tested, and maintainable. Working with a high degree of independence, the engineer moves solutions from prototype to production, partners with senior engineers on architecture, and helps raise the team's technical standards. The role collaborates with business stakeholders and cross-functional partners, contributes to production support and technical design, and evaluates new AI models, tools, and frameworks.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or an equivalent combination of education and experience.
Three to five years of professional software engineering experience, including designing, building, and supporting production applications.
Strong computer science fundamentals, including data structures, algorithms, and system design, and software design skills including API design, data modeling, and design patterns.
Proficiency in Python and TypeScript, with experience building RESTful APIs and front-end applications using a modern framework.
Demonstrated daily use of agentic AI development tools, including the ability to plan, direct, and verify AI-generated work.
Experience building and shipping LLM-powered applications or AI agents, including tool calling, RAG, and prompt and context engineering.
Experience with automated testing, Git-based workflows, code review, and CI/CD pipelines.
Hands-on experience deploying and operating applications on a major cloud platform, including containers and serverless services.
Strong SQL skills and experience with relational databases.
Working knowledge of application security, including OAuth 2.0/OpenID Connect, secrets management, the OWASP Top 10, and LLM-specific risks such as prompt injection.
Ability to work independently through ambiguity, divide problems into deliverable increments, and recognize when to involve senior team members.
Strong communication skills and ability to explain technical decisions and trade-offs to technical and non-technical stakeholders.
Continuous-learning mindset and genuine curiosity about the evolving AI landscape.

Nice To Haves

FastAPI experience.
Angular experience.
Experience with Google ADK, LangGraph, or similar AI frameworks.
Experience with GitHub Actions.
Experience with Google Cloud Platform, including BigQuery or Vertex AI.
Experience with PostgreSQL or another relational database; BigQuery or another analytical data platform experience.
Familiarity with MCP, LLM evaluation and observability practices, and capability, cost, and latency trade-offs across Gemini, Claude, and OpenAI model families.
Experience with infrastructure-as-code such as Terraform, event-driven architectures, C#/.NET, Azure, or property management platforms such as Entrata.

Responsibilities

Translate business requirements into technical solutions, including web applications, APIs, integrations, automated workflows, and AI agents, and own delivery from prototype through production release.
Build backend services and RESTful APIs using Python and FastAPI, and front-end applications using TypeScript and Angular.
Use agentic development tools in daily work for planning, specification, implementation, testing, and documentation; delegate scoped tasks to coding agents and review their results.
Build and maintain shared AI development assets, including agent instruction files, reusable skills and commands, and Model Context Protocol (MCP) servers.
Design, build, and operate AI agents and LLM-powered features using frameworks such as Google ADK or LangGraph, including tool calling, retrieval-augmented generation, structured outputs, and multi-agent patterns where appropriate.
Build evaluation datasets and automated evaluations for AI features, instrument tracing and monitoring, and track production quality, cost, and latency.
Write and maintain unit, integration, and end-to-end tests; contribute to GitHub Actions CI/CD pipelines, automated quality gates, and AI-assisted code review.
Deploy and operate solutions on Google Cloud Platform, including Cloud Run, Cloud SQL, BigQuery, and Vertex AI; use containers and infrastructure-as-code where appropriate.
Participate in production support, troubleshooting, root-cause analysis, and reliability improvements.
Design data models and build integrations with internal and third-party systems using APIs, webhooks, and event-driven messaging, ensuring reliable data flows.
Apply secure-by-design practices, including authentication and authorization, secrets management, least-privilege access for agents and their tools, data protection, and mitigation of AI-specific risks such as prompt injection.
Contribute to architecture and technical design discussions with senior engineers, documenting decisions and trade-offs.
Review human- and AI-authored code, contribute to coding standards, and help mentor associate team members.
Collaborate with business stakeholders and cross-functional partners, including data engineering, to clarify requirements, demonstrate working software early, and communicate technical decisions accessibly.
Participate in sprint ceremonies, provide estimates, and surface technical risks and dependencies early.
Evaluate AI models, tools, and frameworks through proofs of concept, and assess platform, framework, and model updates, including model deprecations, for impact on existing solutions.
Support Cardinal OKRs as needed and participate in Cardinal U training as required.
Champion the Cardinal Culture and Cardinal Core Values.