Sr Software Engineer - AI-First Development

About the role

Job Description: Position Overview The primary responsibility of the Senior Software Engineer (AI-First Development) is to design, orchestrate, and validate software applications built through AI-driven development workflows. This is not an AI-assisted traditional developer role. Rather than writing the majority of code by hand, this role operates within an AI-First Software Development Lifecycle (SDLC) where AI agents serve as the primary producers of code, configuration, and test artifacts. The engineer provides architectural direction, context engineering, human-in-the-loop governance, and final accountability for all delivered software. The Senior Software Engineer combines deep software engineering fundamentals with the ability to think in systems, design effective agent workflows, and validate AI-generated outputs across security, correctness, performance, and compliance dimensions. 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. Essential Duties & Responsibilities Agent Workflow Design and Orchestration Design, build, and maintain AI agent workflows that produce application code, infrastructure configuration, test suites, and documentation. Decompose complex application requirements into discrete, well-scoped tasks that AI agents can execute effectively within defined boundaries. Select and configure appropriate AI models, agent frameworks, and tooling for each workflow based on task complexity, risk level, and cost considerations. Construct and maintain context stores that provide agents with organizational knowledge, coding standards, architectural patterns, and domain context needed to produce correct and consistent outputs. Author and maintain the agent toolchain, including Skills (SKILL.md) for reusable domain knowledge, hooks for deterministic automation at defined workflow points, and project memory files (CLAUDE.md, AGENTS.md) that provide persistent context across agent sessions. Design subagent architectures that decompose complex workflows into specialized, scoped agents with appropriate tool access, following the principle of least privilege for each agent role. Apply compound engineering practices that systematically capture insights, patterns, and failure modes from each development cycle, encoding them into project memory, skills, and agent configurations so that each unit of work makes subsequent work easier and more reliable. Participate in Mob Elaboration sessions to collaboratively refine requirements, acceptance criteria, and context packages before agent execution begins. Verification and Quality Assurance Apply a multi-layer verification framework to all AI-generated outputs, validating functional correctness, security posture, performance characteristics, code quality, and regulatory compliance. Establish and enforce human-in-the-loop (HITL), on-the-loop (OHOTL), and after-the-loop (AHOTL) governance checkpoints appropriate to the risk level of each workflow. Review, test, and approve AI-generated code, ensuring it meets Sands coding standards, architectural guidelines, and security requirements before promotion to production. Design and maintain automated verification pipelines that supplement human review, including test harnesses, static analysis gates, and runtime telemetry. Identify and remediate patterns of agent drift, hallucination, or quality degradation across repeated workflow executions. Implement agent observability and telemetry systems that track agent behavior, tool call patterns, token consumption, and output quality metrics across workflows. Application Development and Architecture 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 the foundational context for agent-driven development. Collaborate 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 agent workflows, coding standards, and verification practices. Maintain the ability to write, debug, and refactor code directly when agent outputs require manual intervention or when exploring novel architectural approaches. Ensure all delivered applications meet enterprise standards for scalability, maintainability, observability, and operational readiness. Design and build custom MCP servers that expose internal tools, databases, and business systems to AI agents through standardized interfaces, enabling agents to interact wit

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