Principal Developer - AI

United Airlines•Houston, TX

7d•$140,600 - $183,108

About The Position

United Airlines’ Digital Technology team builds and maintains large-scale technology solutions using innovative architectures, data analytics, and digital solutions. The Principal Developer is a hands-on individual contributor responsible for designing, building, testing, deploying, and supporting AI agents and the software capabilities that enable them. The role connects United capabilities—including Flight Shopping, Shopping Cart, and Reference Data—to AI-enabled experiences through secure, reusable tools, APIs, and agent services. The position combines direct software development with technical leadership. The Principal Developer translates business needs into working solutions and establishes engineering patterns through implementation. They partner with product, architecture, security, and engineering teams, providing technical direction, reviewing code, and mentoring developers while remaining accountable for delivery. The role also evaluates emerging AI technologies, validates them through hands-on experiments, and turns successful approaches into maintainable production capabilities. It involves aligning technical work with business needs, coordinating integrations and standards, and communicating decisions and operational guidance to stakeholders.

Requirements

Bachelor’s degree.
Seven years of software development experience, including substantial hands-on experience building APIs, distributed services, and enterprise integrations, with at least three years performing senior- or principal-level development responsibilities.
Demonstrated hands-on experience building and deploying LLM-based applications or AI agents that invoke tools and integrate with business systems.
Strong software engineering skills, including proficiency in Python and experience with enterprise development technologies such as C#/.NET.
Practical experience implementing AI agents, tool/function calling, agent workflows, and integration with enterprise APIs.
Ability to translate technical designs into tested, deployable, maintainable code and independently troubleshoot complex issues.
Experience designing and building APIs, microservices, and distributed systems.
Experience with cloud services, preferably AWS, and automated build, test, and deployment pipelines.
Understanding of AI-specific risks, including prompt injection, unintended tool execution, sensitive data exposure, and unreliable model outputs, and experience implementing appropriate controls.
Experience with automated testing, agent evaluations, observability, performance tuning, and production support.
Ability to provide technical direction through implementation, code reviews, and mentoring.
Strong problem-solving and communication skills, including the ability to explain technical tradeoffs and collaborate across teams.

Nice To Haves

Experience implementing Model Context Protocol (MCP) servers or comparable standards for connecting AI applications to enterprise tools.
Experience with agent orchestration frameworks, model providers, and evaluation tooling.
Experience with OAuth/OIDC, API gateways, rate limiting, idempotency, and secure transaction processing.
Experience with airline shopping, retail, cart, booking, servicing, or comparable transactional systems.
Experience progressing AI experiments into supported production capabilities.
A degree in Computer Science, Information Technology, Engineering, or a related field is preferred.

Responsibilities

Design, write, test, deploy, and support software for AI agents, agent workflows, tool integrations, and supporting services.
Build reusable tools and APIs that expose existing business capabilities to authorized AI agents and applications.
Implement agent capabilities such as tool selection and execution, context management, workflow state, error recovery, and appropriate human approval.
Develop production-ready integrations with existing systems, applying authentication, authorization, data protection, input validation, and transaction safeguards.
Own assigned capabilities through the full development lifecycle, including automated testing, deployment, monitoring, troubleshooting, and ongoing improvement.
Establish technical patterns through working implementations, conduct code reviews, and mentor developers.
Measure and optimize reliability, response quality, latency, scalability, and operating cost.
Evaluate emerging AI models, agent frameworks, and integration protocols through focused, hands-on experiments.
Build proofs of technology to validate feasibility, expose limitations, and inform implementation decisions.
Translate successful experiments into reusable engineering components and maintainable production solutions.
Develop evaluation approaches that measure agent behavior, tool execution accuracy, security, and business outcomes.
Recommend technology choices based on demonstrated results, integration needs, maintainability, and cost.
Translate business needs into clear technical requirements, acceptance criteria, and deliverable increments with product and engineering partners.
Collaborate with architecture, security, platform, and application owners to resolve integration dependencies and implement agreed standards.
Participate in design discussions, backlog refinement, and sprint planning, contributing implementation experience and practical technical recommendations.
Communicate progress, technical tradeoffs, risks, and decisions to technical and nontechnical stakeholders.
Document interfaces, implementation decisions, and operational guidance so other teams can adopt and support delivered capabilities.