Senior Software Engineer, Full Stack Web Development – GCP & Vertex AI Focus

GM•Austin, TX

2d•Hybrid

About The Position

The Brands & Marketing Software Engineering Team within the Digital Products Engineering (DPE) organization at GM is looking for an exceptional Senior Software Engineer (Full Stack Web) to build high-impact web products and design intelligent search and discovery capabilities on Google Cloud Platform using Vertex AI Search. The team collaborates closely with marketing leaders to build easy-to-use tooling that powers engaging and innovative customer experiences at scale, including AI-powered discovery and search experiences. The mission is to rapidly ideate, iterate, and launch next-generation solutions to connect GM with customers, increasingly leveraging GCP and Vertex AI Search for intelligent, personalized digital experiences. The team comprises industry-leading engineers who utilize innovative development methodologies and technologies.

Requirements

Bachelor’s degree in Computer Science or related field, or equivalent experience
5+ years of web application development experience
Strong frontend and backend development skills, including HTML, CSS, JavaScript, and Java, Python, and/or server-side JavaScript
Hands-on experience designing and operating production workloads on GCP, including services such as Cloud Run, GKE, Cloud Functions, Pub/Sub, Cloud Storage, Cloud SQL/Spanner, and BigQuery
Experience with AI-powered search or recommendation systems, ideally Vertex AI Search or similar semantic/vector search platforms
Experience with system design, testing, debugging, automation, and performance optimization

Nice To Haves

Master’s degree in Computer Science or related field
7+ years of experience building scalable, high-traffic web applications and services
Experience with ReactJS
Experience with Adobe Experience Manager, Akamai, and/or Microsoft Azure
Experience with Helm, Terraform, and ArgoCD
Direct experience with Vertex AI Search, including schemas, indexing, ingestion pipelines, tuning, and evaluation
Experience with LLM/embedding-based retrieval, RAG, experimentation, and personalization.

Responsibilities

Develop competency across our complete web technologies stack (client, framework, and services).
Produce high quality software that is unit tested, reviewed, and checked in regularly for continuous integration and deployment.
Serve as a tech lead, actively mentoring other engineers on the team and helping drive technical decisions and tradeoffs.
Investigate and resolve performance bottlenecks across the full stack, including browser, edge, and cloud services.
Lead efforts to automate testing, quality gates, and delivery pipelines, with a strong focus on reliability and observability.
Work on API, Content Management, edge, and cloud systems that power GM brand and marketing experiences.
Leverage technical leadership to ensure adherence to engineering best practices and evangelize opportunities to improve engineering productivity and craftsmanship.
Design and build web applications and services on GCP, leveraging services such as Cloud Run, GKE, Cloud Functions, Pub/Sub, Cloud Storage, Cloud SQL/Spanner, and BigQuery.
Implement and integrate Vertex AI Search to power intelligent search, semantic retrieval, and content discovery across GM’s digital products.
Define and evolve search schemas, indexing pipelines, and relevance signals to continuously improve search quality and user satisfaction.
Collaborate with data science and ML partners to productionize and iterate on search and recommendation models using Vertex AI, including experimentation frameworks and A/B tests.
Build robust, secure APIs and backend services that expose search and recommendation capabilities to web and mobile clients.
Partner with product and UX to design search-centric user experiences informed by analytics and experimentation.
Lead the design and development of an enterprise-wide search platform that unifies discovery across brands, channels, and content sources, starting with a Vertex AI Search–based implementation.
Define clear abstraction layers, contracts, and interfaces so that the core search capabilities can be swapped or extended to other search engines in the future with minimal impact to client applications and user experience.