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About This Role
Position Overview
The primary responsibility of the Senior Software Development Engineer in Test (AI\-First Development) is to design, orchestrate, and validate the automated verification systems that gate every change shipped through the AI\-First Software Development Lifecycle (SDLC). This role operates within an AI\-First SDLC in which AI agents serve as primary producers of code, configuration, and test artifacts, while the Senior Software Development Engineer in Test (SDET) provides test strategy, context engineering, human\-in\-the\-loop governance, and final accountability for the quality of delivered software.
The Senior SDET is an experienced quality engineer or test automation engineer who has adopted modern AI\-assisted development tools as a core part of their daily workflow and is prepared to grow into deeper agent orchestration, context engineering, and verification responsibilities.
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
- Test Strategy, Agent Workflow Design, and Orchestration
+ Design, build, and maintain AI agent workflows that produce unit, integration, end\-to\-end, performance, and security test suites from specifications and intent documents.
+ Decompose acceptance criteria and technical contracts into discrete, verifiable test scenarios that AI agents can execute effectively within defined boundaries.
+ Select and configure appropriate AI models, agent frameworks, and test\-generation tooling for each workflow based on risk level, coverage requirements, and cost considerations.
+ Construct and maintain test context that provides agents with test patterns, fixture strategies, data classification rules, and domain information needed to produce correct and consistent test outputs.
+ Contribute to the testing toolchain, including reusable test skills, automation hooks, and project memory files that provide persistent context across agent sessions. Authoring of advanced toolchain components may be developed on the job.
+ Systematically capture defect patterns, escape modes, and verification failures from each development cycle and encode them back into shared context, test skills, and agent configurations so that subsequent work becomes more reliable.
+ Participate in collaborative refinement sessions to align on acceptance criteria, technical contracts, and test context packages before agent execution begins.
- Verification and Quality Assurance
+ Operate the multi\-layer Verification Framework on every pull request, with primary ownership of the automated testing layer, validating functional correctness, security posture, performance characteristics, code quality, and regulatory compliance.
+ Apply human oversight at governance checkpoints appropriate to the risk level of each workflow, including pre\-execution review, in\-flight observation, and post\-execution audit.
+ Review, test, and approve AI\-generated code and test suites, ensuring they meet Sands testing standards, architectural guidelines, and security requirements before promotion to production.
+ Verify that AI\-generated tests exercise specified intent rather than mirroring implementation, and reject suites that pass without actually exercising the behavior the specification asked for.
+ Support independent QA verification after merge, contributing to system, integration, and regression testing in production\-like environments and partnering with the QA Lead on User Acceptance Testing coordination where applicable.
+ Support agent observability practices that track test behavior, flakiness signals, coverage trends, and defect escape rates across workflows.
- Test Engineering and Architecture
+ Architect and deliver scalable test automation frameworks across web, API, mobile, and data layers using AI\-First methodologies as the primary development approach.
+ Define test data strategies, test environment patterns, and verification contracts that serve as foundational context for agent\-driven test generation.
+ Collaborate with cross\-functional teams including engineering, product, infrastructure, and security to translate business requirements into executable verification workflows.
+ Coordinate with QA and engineering teams across global locations to ensure consistency in testing standards and verification practices.
+ Write, debug, and refactor test code directly when agent outputs require manual intervention or when designing novel test approaches.
+ Ensure delivered test automation meets enterprise standards for reliability, maintainability, observability, and operational readiness.
- Continuous Improvement and Mentorship
+ Evaluate emerging AI models, agent frameworks, and test automation tools to continuously improve verification effectiveness and output quality.
+ Mentor team members on AI\-assisted testing practices, context engineering techniques, and verification methodologies.
+ Document test patterns, prompt and context libraries, and lessons learned to build institutional knowledge.
+ Participate in collaborative construction sessions, guiding agent execution in real time and coaching team members on effective test orchestration techniques.
- 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.
- 5\+ years of professional software development or test automation experience, including time in senior or lead positions where the candidate owned the design and delivery of non\-trivial verification systems.
- 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 hands\-on experience with at least one modern test automation framework (such as Playwright, Cypress, REST Assured, pytest, or comparable). Familiarity with additional layers (UI, API, integration) is a plus.
- Experience deploying and operating test automation on at least one major cloud platform (Azure, AWS, or GCP). Azure experience is a plus given the LVS footprint but is not required.
- Working knowledge of DevOps practices and CI/CD pipelines, including familiarity with how automated test gates fit into a build pipeline.
- Demonstrated experience conducting thorough code reviews, identifying defects in both human\- and AI\-generated outputs, and providing constructive technical feedback.
- 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 test workflows.
- Exposure to one or more specialized testing approaches such as performance and load testing (K6, JMeter, Gatling), contract testing, mutation testing, or chaos engineering. Proficiency in any one is a plus; depth across all is not expected.
- Knowledge of secure testing practices, OWASP guidelines, and SAST/DAST/SCA tooling, and experience working within a regulated industry such as gaming, finance, healthcare, or hospitality. Understanding of data privacy and responsible AI principles.
- Working knowledge of relational or non\-relational databases sufficient to model test data, design verification queries, and reason about query performance.
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 Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Las Vegas Sands Corp., this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 610 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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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 Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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