Principal Software Engineer - AI-First Development

Las Vegas Sands Corp. • Las Vegas, NV

7d • Remote

About The Position

The primary responsibility of the Principal Software Engineer (AI-First Development) is to direct the day-to-day technical execution of a small AI-First engineering team, designing, orchestrating, and validating software applications built through AI-driven development workflows. This role operates within an AI-First Software Development Lifecycle (SDLC) in which AI agents serve as primary producers of code, configuration, and test artifacts, while the Principal Software Engineer provides architectural direction, context engineering, human-in-the-loop governance, technical mentorship, and final accountability for delivered software. The Principal Software Engineer is a seasoned engineer who has already integrated modern AI-assisted development tools into their daily workflow and who has experience guiding other engineers through architectural decisions, code reviews, and delivery commitments.

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.
• Must be able to obtain and maintain any certification or license, as required by law or policy.
• 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.
• Demonstrated daily use, over the past 6 months or more, of at least one modern AI-assisted development tool such as Claude Code, Cursor, GitHub Copilot, or Windsurf, with the ability to speak concretely about effective usage patterns and failure modes.
• Strong foundational knowledge in at least one major programming ecosystem (such as .NET/C#, JavaScript/TypeScript, Python, Java, or Go) and the ability to read, evaluate, and validate code in additional languages relevant to a given project.
• Working knowledge of relational and non-relational databases, including data modeling, query performance, and schema design.
• Experience deploying and operating services on at least one major cloud platform (Azure, AWS, or GCP). Azure experience is a plus.
• 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.

Nice To Haves

• 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.

Responsibilities

• Define, build, and maintain the AI agent workflows the team uses to produce application code, infrastructure configuration, test suites, and documentation, and guide other engineers in extending them.
• Decompose application requirements into discrete, well-scoped tasks that AI agents can execute effectively within defined boundaries, and review task decomposition produced by team members.
• Select and configure appropriate AI models, agent frameworks, and tooling for each workflow based on task complexity, risk level, and cost considerations, and set the defaults the team works from.
• Construct and maintain shared context that provides agents with organizational knowledge, coding standards, architectural patterns, and domain information needed to produce correct and consistent outputs.
• Own the team's agent toolchain, including reusable skills, automation hooks, MCP integrations, and project memory files that provide persistent context across agent sessions.
• Apply scoped subagent patterns where appropriate, following the principle of least privilege for tool access, and coach engineers on when multi-agent architectures are warranted versus when simpler workflows suffice.
• Systematically capture insights, patterns, and failure modes from each development cycle and encode them back into shared context, skills, and agent configurations so that subsequent work becomes more reliable.
• Lead collaborative requirement refinement sessions to align the team on acceptance criteria and context packages before agent execution begins.
• Apply and uphold a multi-layer verification approach to AI-generated outputs, validating functional correctness, security posture, performance characteristics, code quality, and regulatory compliance.
• Set the human oversight expectations at governance checkpoints appropriate to the risk level of each workflow, including pre-execution review, in-flight observation, and post-execution audit, and verify the team is operating to them.
• Serve as the final reviewer and approver of AI-generated code for non-trivial changes, ensuring it meets Sands coding standards, architectural guidelines, and security requirements before promotion to production.
• Build and maintain automated verification pipelines that supplement human review, 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 as the primary development approach.
• Define system architecture, data models, API contracts, and integration patterns that serve as foundational context for agent-driven development, acting as the technical authority within the team on these decisions.
• Partner with cross-functional teams including product, design, infrastructure, and security to translate business requirements into executable agent workflows.
• Coordinate with development teams across global locations to 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 while the formal manager-of-record sits elsewhere in the organization.
• 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, accelerating the growth of less experienced engineers on the team.
• 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.
• Perform job duties in a safe manner.
• Attend work as scheduled on a consistent and regular basis.
• Perform other related duties as assigned.

Benefits

• All duties are to be performed in accordance with departmental and Las Vegas Sands Corp.’s policies, practices, and procedures.
• All Las Vegas Sands Corp. Team Members are expected to conduct and carry themselves in a professional manner at all times.
• Team Members are required to observe the company’s standards, work requirements and rules of conduct.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior