Principal Software Engineer - AI-First Development

Las Vegas Sands•Las Vegas, NV

5d•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. 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.

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.