Principal Software Engineer - AI-First Development

About the role

The Principal Software Engineer (AI-First Development) leads a small AI-First engineering team, directing the day-to-day technical execution of the team.

Responsibilities

• Define, build, and maintain AI agent workflows for producing application code, infrastructure configuration, test suites, and documentation.
• Decompose application requirements into discrete tasks for AI agents, and review task decomposition produced by team members.
• Select and configure AI models, agent frameworks, and tooling for each workflow based on task complexity, risk level, and cost considerations.
• Create and maintain shared context for agents, including organizational knowledge, coding standards, architectural patterns, and domain information.
• Owning the team's agent toolchain, including reusable skills, automation hooks, MCP integrations, and project memory files.
• Systematically capture insights, patterns, and failure modes from each development cycle and encode them back into shared context, skills, and agent configurations.
• Lead collaborative requirement refinement sessions to align the team on acceptance criteria and context packages before agent execution.
• Apply and uphold a multi-layer verification approach to AI-generated outputs, including functional correctness, security posture, performance characteristics, code quality, and regulatory compliance.
• Set human oversight expectations at governance checkpoints and verify the team is operating to them.
• Build and maintain automated verification pipelines, including test harnesses, static analysis gates, and runtime telemetry.
• Identify and lead remediation of patterns of agent drift, hallucination, or quality degradation across repeated workflow executions.
• Define the team's agent observability practices, tracking behavior, tool call patterns, token consumption, and output quality across workflows.
• Architect and deliver full-stack applications across web, API, and data layers using AI-First methodologies.
• Translate business requirements into executable agent workflows, coordinate with cross-functional teams, and ensure consistency in coding standards and verification practices.
• Write, debug, and refactor code directly when agent outputs require manual intervention or when exploring novel architectural approaches.
• Ensure delivered applications meet enterprise standards for scalability, maintainability, observability, and operational readiness.
• Direct the day-to-day technical execution of a small AI-First engineering team, providing dotted-line technical leadership.
• Evaluate emerging AI models, agent frameworks, and development tools to continuously improve workflow effectiveness and output quality.
• Mentor team members on AI-assisted development practices, context engineering techniques, and verification methodologies.
• Contribute to the evolution of the Sands AI-First SDLC standard, proposing refinements based on practical experience and measurable outcomes.
• Document workflow patterns, prompt and context libraries, and lessons learned to build institutional knowledge.
• Monitor and optimize token consumption and cost across the team's agent workflows, applying strategies such as plan mode, context editing, and efficient context window management.
• Lead collaborative construction sessions, guiding agent execution in real time and coaching team members on effective orchestration techniques.
• Participate in hiring activities for the team, including resume review, technical interviews, and onboarding new engineers.

Requirements

• At least 21 years of age.
• Proof of authorization to work in the United States.
• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
• 8+ years of professional software development experience, including time in senior, lead, or staff positions owning the design and delivery of non-trivial systems.
• Demonstrated experience providing technical leadership to a small engineering team, including running code reviews, mentoring engineers, and driving delivery without necessarily holding the formal people-manager role.
• Strong foundational knowledge in at least one major programming ecosystem and the ability to read, evaluate, and validate code in additional languages relevant to a given project.
• Experience deploying and operating services on at least one major cloud platform (Azure, AWS, or GCP).
• Working knowledge of DevOps practices, CI/CD pipelines, and infrastructure-as-code concepts.
• Demonstrated ability to conduct thorough code reviews, identify defects in both human- and AI-generated outputs, and provide constructive technical feedback to engineers at multiple experience levels.
• Excellent written and verbal communication skills, with the ability to articulate technical decisions and trade-offs to both technical and non-technical stakeholders.
• Strong interpersonal skills with the ability to communicate effectively and interact appropriately with management, other Team Members, and outside contacts of different backgrounds and levels of experience.

Qualifications

• Practical experience constructing structured context for LLMs, including prompt design, RAG pipelines, context window optimization, project memory files (such as CLAUDE.md or AGENTS.md), and integration with MCP servers.
• Familiarity with tactical context management techniques such as plan mode, context editing, and multi-session splitting.
• Experience authoring reusable skills, configuring automation hooks, building custom MCP servers, or otherwise assembling agent toolchains that enable repeatable, production-grade workflows.
• Prior experience standing up or leading an AI-First or agent-driven development practice on a team, with measurable outcomes around delivery speed, quality, or cost.
• Experience with microservices, event-driven architectures, or message-based systems (such as Kafka, RabbitMQ, or Azure Service Bus), and an understanding of enterprise integration patterns at scale.
• Knowledge of secure development practices and OWASP guidelines, and experience working within a regulated industry such as gaming, finance, healthcare, or hospitality.
• Understanding of data privacy and responsible AI principles.
• Experience with unit, integration, and end-to-end testing frameworks, and the ability to evaluate AI-generated test coverage and identify gaps.