Artificial Intelligence Engineer

About the role

Principal Software Engineer - IT - United States

Fully onsite in Round Rock, TX

Description:

ACCOUNTABILITIES: Designs, codes, tests, debugs, and documents software according to systems quality standards, policies and procedures. Analyzes business needs and creates software solutions. Responsible for preparing design documentation. Prepares test data for unit, string and parallel testing. Evaluates and recommends software and hardware solutions to meet user needs. Resolves customer issues with software solutions and responds to suggestions for improvements and enhancements. Works with business and development teams to clarify requirements to ensure testability. Drafts, revises, and maintains test plans, test cases, and automated test scripts. Executes test procedures according to software requirements specifications. Logs defects and recommends fixes. Retests software corrections to ensure problems are resolved. Documents evolution of testing procedures for future replication. May conduct performance and scalability testing.

RESPONSIBILITIES: Plans, conducts and leads assignments generally involving moderate, high budgets projects or more than one project. Manages user expectations regarding appropriate milestones and deadlines. Assists in training, work assignment and checking of less experienced developers. Serves as technical consultant to leaders in the IT organization and functional user groups. Subject matter expert in one or more technical programming specialties; employs expertise as a generalist of a specialist. Performs estimation efforts on complex projects and tracks progress. Works on the highest level of problems where analysis of situations or data requires an in-depth evaluation of various factors. Documents, evaluates and researches test results; documents evolution of testing scripts for future replication. Identifies, recommends and implements changes to enhance the effectiveness of quality assurance strategies.

Responsibilities

Design, build, and deploy AI-powered capabilities across the SDLC, including:

Spec-driven development workflows that support the translation of well-formed specifications into secure, verifiable implementations

Assurance of AI-generated code - guardrails, policy enforcement, and verification for code produced by AI assistants and agents

SDLC skills and agent tooling - developer-assist skills as well as verification skills that perform automated security checks (design review, dependency and supply-chain analysis, static/dynamic analysis orchestration, release audit support)

Integrate solutions with enterprise systems - source control, CI/CD, ticketing, security scanning, identity, and internal platforms - through APIs, webhooks, and protocols such as MCP (Model Context Protocol)

Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate trade-offs, and support solution adoption

Apply sound architecture and systems design practices: well-defined service boundaries, appropriate data models, secure defaults, observability, and extensibility

Required Skills and Qualifications

Demonstrated experience developing and deploying AI-based solutions in production environments, with measurable business or operational impact

Strong programming proficiency (e.g., Python, TypeScript/JavaScript, Go, or similar) and adherence to software engineering best practices, including testing, code quality, and maintainability

Hands-on experience with modern AI/LLM development, including:

Context engineering - designing what informs the model's context window, including agentic retrieval and search, memory architectures, grounding in enterprise data, and structured outputs

Agentic system design - agent loop engineering, multi-agent and sub-agent orchestration, and tool/function calling

Context window management and token budgeting, including cost and latency optimization for production workloads

Evaluation of AI system quality, reliability, and safety

Solid understanding of software architecture and systems design, including API design, event-driven patterns, and data modeling for scalability and extensibility

Experience developing and/or deploying applications with large-scale impact (broad user base, high transaction volume, or organization-wide adoption)

Experience integrating with multiple systems and platforms (REST/GraphQL APIs, CI/CD pipelines, cloud services, enterprise tooling)

Demonstrated ability to work independently across the full delivery lifecycle - requirements analysis, solution design, implementation, deployment, and stakeholder engagement - with accountability for results

Strong communication and collaboration skills, with the ability to convey technical concepts to both engineering and business audiences

Working knowledge of secure development practices and experience designing solutions that meet enterprise security and compliance requirements