Overview
Focus is on advancing the use of artificial intelligence and machine learning technologies across core product development and engineering platforms.
This work requires a hands-on developer to actively utilize modern AI tools and coding assistants to accelerate software delivery across the entire Software Development Lifecycle (SDLC)—from initial requirements analysis to development, testing, deployment, and monitoring. Responsibilities include designing, building, and maintaining secure, scalable web applications utilizing Angular on the frontend and cloud-native services on Google Cloud Platform (Google Cloud Platform) on the backend.
Technical Skills Quick-Glance
| Area | Technologies |
| Frontend | Angular (v14+ preferred), TypeScript, HTML, CSS/SCSS, RxJS, NgRx, design systems |
| Backend & APIs | Node.js, Java, Python, Spring Boot, NestJS, Express, REST APIs, Microservices |
| Cloud & DevOps | Google Cloud Platform (Cloud Run, GKE, Cloud Functions, Pub/Sub, Cloud SQL, Secret Manager, Cloud Build), Docker, Git, CI/CD |
| AI & LLM Integration | AI Coding Assistants, LLM APIs, Vector Search/Databases, RAG, Agentic Workflows |
Core Responsibilities
1. Full-Stack Application Development
* Design, develop, test, and maintain robust full-stack web applications using Angular, modern backend services, and cloud-native technologies on Google Cloud Platform.
* Create highly responsive, intuitive, and performant user interfaces that simplify complex engineering workflows.
* Develop secure backend services, RESTful APIs, data integrations, and reusable software components.
* Integrate applications with enterprise data sources, authentication systems, and internal engineering tools.
* Ensure all production applications are secure, scalable, highly observable, and easily maintainable.
2. AI-Accelerated Software Delivery (SDLC)
* Utilize AI coding assistants and generative AI platforms to streamline development, refactoring, debugging, documentation, and code reviews.
* Leverage AI capabilities to assist with technical design exploration, user story refinement, and test-case generation (unit, integration, and end-to-end testing).
* Apply AI-assisted approaches to accelerate defect triage, log analysis, root-cause investigation, and production support workflows.
* Drive the responsible and secure use of AI tools in alignment with enterprise security and data privacy standards.
3. Cloud-Native & Platform Engineering
* Deploy and support containerized applications using Google Cloud Platform cloud-native architecture patterns.
* Collaborate with infrastructure and DevSecOps teams to build, manage, and optimize CI/CD pipelines, containerization (Docker/Kubernetes), and automated deployments.
* Apply cloud security best practices, including robust identity and access management (IAM), secrets management, and data protection controls.
4. AI and Workflow Integration
* Help build and enhance internal developer productivity platforms and AI-enabled software tools.
* Integrate applications with LLMs, custom embeddings, vector databases, and workflow automation frameworks.
* Explore and implement emerging AI patterns, including Retrieval-Augmented Generation (RAG), agentic workflows, and Model Context Protocol (MCP).
Required Qualifications
* Education: Bachelor’s Degree in Computer Science, Software Engineering, or a related technical field (or equivalent professional experience).
* Experience: 7+ years of professional full-time software development experience.
* Frontend Expertise: 7+ years of hands-on experience building modern, large-scale web applications using Angular and TypeScript.
* Backend Development: Strong experience developing backend APIs and microservices using languages/frameworks such as Node.js, Python, Java, Spring Boot, or NestJS.
* Cloud Platform: Hands-on development experience with Google Cloud Platform (Google Cloud Platform) or an equivalent major cloud platform (AWS/Azure) with a strong willingness to work in a Google Cloud Platform environment.
* AI Tooling: Practical, modern experience utilizing AI coding assistants (e.g., GitHub Copilot, Cursor) or LLM-driven development workflows to optimize productivity.
* Engineering Best Practices: Proven track record with Git, CI/CD pipelines, automated testing, secure coding practices, and Agile delivery.
Preferred Qualifications
* Deep expertise in Angular architecture, state management (RxJS, NgRx), and frontend performance optimization.
* Practical experience deploying applications utilizing specific Google Cloud Platform services such as Cloud Run, GKE, Cloud Build, BigQuery, Pub/Sub, and Cloud SQL.
* Experience building production-grade AI-enabled applications (using LLM APIs, prompt engineering, vector search, or LangChain/Semantic Kernel frameworks).
* Familiarity with unit and integration testing frameworks (e.g., Jasmine, Jest, Cypress, Playwright, PyTest).
* Background working in large-scale enterprise environments with rigorous compliance, security, and data governance policies.
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