Senior Advanced Product Engineer

Scientific Games•Alpharetta, GA

About The Position

Scientific Games is hiring two Senior Product Engineers to join a small, high-impact team focused on strategic product initiatives and new business opportunities. This role is ideal for experienced builders who can flex across product and engineering from zero-to-one development to enhancing complex, legacy systems. You’ll be expected to deliver high-quality software quickly, make sound technical decisions, and leave systems better than you found them. The team operates like a fast-moving product engineering pod, leveraging AI to accelerate development while maintaining long-term quality through clear specs, strong testing, and production-safe releases. Successful candidates are product-minded, pragmatic, and technically strong. They focus on building the right solutions, earn trust through outcomes, and deliver work that is scalable, maintainable, and impactful.

Requirements

10+ years of Software Engineering experience
Experience building production software across the full lifecycle: product framing, design, implementation, testing, release, operations, and iteration.
Experience working in large or legacy codebases while improving architecture, testability, observability, and delivery speed.
Hands-on fluency with modern AI-assisted development tools, including coding assistants, agentic workflows, AI-assisted code generation and review, test generation, documentation support, refactoring, migration support, and debugging.
Strong automated testing discipline, including TDD, ATDD, unit testing, integration testing, contract testing, regression automation, and production validation.
Experience building fast feedback loops with CI/CD, automated test suites, feature flags, preview environments, observability, deployment automation, and progressive release practices.
Strong engineering judgment across software design, APIs, integration patterns, data flows, reliability, security, and production readiness.
Ability to work directly with product leaders, business stakeholders, domain experts, QA, DevOps, architecture, security, and operations teams.
Clear written and verbal communication; able to turn ambiguity into an implementation path others can understand.

Nice To Haves

Product engineering in a startup, growth-stage company, incubation team, platform team, or strategic product pod.
Experience bringing modern development practices into older systems without stopping delivery.
Regulated, high-reliability, transactional, gaming, lottery, payments, or customer-facing platform environments.
Cloud-native development, platform engineering, infrastructure as code, APIs, event-driven systems, distributed systems, or integration-heavy architectures.
Experience helping other engineers adopt better AI-assisted development, testing, release, or observability practices.

Responsibilities

Build new products, prototypes, integrations, and production capabilities tied to strategic business opportunities.
Work inside existing legacy codebases while improving testability, interfaces, observability, automation, and maintainability.
Use AI coding tools and agentic workflows to accelerate code generation, test creation, documentation, refactoring, migration work, debugging, and review.
Turn ambiguous product intent into clear specs, acceptance criteria, interface contracts, examples, test plans, and release criteria.
Practice disciplined automated testing, including TDD, ATDD, unit tests, integration tests, contract tests, regression tests, and production validation where appropriate.
Create fast feedback loops through CI/CD, feature flags, preview environments, observability, automation, and production-safe release patterns.
Partner with product, architecture, QA, DevOps, security, operations, and domain experts to make practical tradeoffs and get work into use.
Reduce cycle time by removing ambiguity, waiting, brittle test paths, slow reviews, unclear ownership, and avoidable rework.
Help define how this team works: engineering standards, technical decisions, AI-assisted development patterns, test strategy, and production readiness.
Share useful patterns with other engineering teams so the work improves more than one product or codebase.