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About This Role
Position Overview
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.
Essential Duties \& Responsibilities
- Agent Workflow Design and Orchestration
+ 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.
- Verification and Quality Assurance
+ 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.
- 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 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.
- Continuous Improvement and Mentorship
+ 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.
Minimum Qualifications
- 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.
Preferred 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.
Physical Requirements
Must be able to:
- Physically access assigned workspace areas with or without reasonable accommodation.
- Work remotely as necessary.
- Work indoors and be exposed to various environmental factors such as, but not limited to, CRT, noise, and dust.
- Utilize laptop and standard keyboard to perform essential functions of the job.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At Las Vegas Sands Corp., this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Las Vegas Sands Corp. AI Hiring
Las Vegas Sands Corp. has 4 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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