Fullstack Developer (AI)
Builds AI-powered full-stack applications by integrating machine learning models with front-end interfaces and APIs, focusing on seamless user experiences and scalable back-end systems.
- Compensation
- Not disclosed
- Location
- Taguig, Philippines
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Fullstack
- Posted
Skills
Must have
- 3–5 years in Full Stack Development with at least 1–2 years in AI-integrated products
- Bachelor’s degree in Computer Science or Software Engineering
- >3 years in IT and Telco industry experience
- >3 years in Web/Mobile App Projects
- >3 years coordinating/managing vendors
- >1 year in Testing Automation
- Basic knowledge of project management discipline
- Basic knowledge of DevOps Tools (CI/CD, Terraform, JIRA, Confluence)
- Front-end: React.js, Next.js, or Vue.js; Tailwind CSS
- Back-end: Python (FastAPI/Flask) or Node.js
- AI/ML: LangChain, LlamaIndex, OpenAI SDK, basic PyTorch/TensorFlow
- Databases: PostgreSQL, Vector DBs (Pinecone, Weaviate, Chroma)
- Cloud: Google Kubernetes Engine, Cloud Functions, DFCX
Full description
At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.
Enables tight integration between application layers and machine learning models. This role is responsible for building both the "brain" (AI models/logic) and the "body" (user interface and APIs) of an application, ensuring that AI features are not just functional but seamlessly interconnected into the user experience.
The AI Full Stack Developer operates across the full application stack, bridging model development and application production deployment, with an emphasis on scalability, integration, and usability.
Responsibilities
- End-to-End Development: Building responsive front-end interfaces that interact with sophisticated AI back-end, MCP Fulfilment Servers and Knowledge Management System
- AI Integration: Implementing RAG (Retrieval-Augmented Generation) architectures and connecting LLMs to real-time data sources.
- API Design: Creating the bridges (REST/GraphQL) that allow front-end components to communicate with ML models.
- Product Realization: Taking a raw AI model and turning it into a polished, user-facing software product.
- Participate in cross-functional brainstorming, strategic planning and business review sessions
- Oversee best practices implemented during the entire DevOps/MLOps LifeCycle
- Managed Resources like Code Repositories, API Optimization, Cloud Resources, CI/CD and Data Pipeline
Nature of Problems Encountered
- Latency Management: Solving the "slow AI" problem—ensuring users aren't left waiting while a model processes a complex request.
- Prompt Engineering vs. UI: Aligning the "hidden" logic of an AI prompt with the visible inputs provided by a user.
- State Management: Handling the complex states of an AI conversation (memory, context windows, and history).
- Intermittent Failures: Debugging non-deterministic errors where an AI model provides a different (or wrong) output for the same input.
Requirements
Technical Stack:
- Front-end: React.js, Next.js, or Vue.js; Tailwind CSS
- Back-end: Python (FastAPI/Flask) or Node.js.
- AI/ML: LangChain, LlamaIndex, OpenAI SDK, and basic knowledge of PyTorch/TensorFlow.
- Databases: PostgreSQL, and Vector DBs like Pinecone, Weaviate, or Chroma.
- Cloud Infrastructure: Google Kubernetes Engine, Cloud Functions, DFCX