As an experienced Full-Stack AI Engineer, you will design, build, and deploy end-to-end, AI-native applications. You will own the entire feature lifecycle—from designing intuitive frontend user interfaces that handle streaming AI data, to building robust backend architectures, orchestrating complex LLM or agentic workflows, and managing vector databases and cloud infrastructure. You are a generalist with architectural breadth and deep specialization in applying AI to practical software challenges.
Make an Impact by:
- Applied AI & Intelligent Orchestration
- Orchestration & Workflow Design: Build complex multi-agent frameworks, task routing systems, and Human-in-the-Loop (HITL) workflows using orchestration engines like LangChain or LangGraph.
- RAG & Vector Management: Architect high-performance Retrieval-Augmented Generation (RAG) pipelines. Design indexing strategies, chunking mechanisms, and hybrid search (semantic + keyword) utilizing vector databases.
- Model Integration & Optimization: Integrate state-of-the-art LLMs and foundational models via commercial APIs or self-hosted inference setups. Implement strategies to manage model latency, token constraints, context windows, and fallback mechanisms.
- Evaluation & Guardrails: Establish AI evaluation frameworks to test for hallucinations, drift, and accuracy. Implement prompt safety, structural validation, and robust guardrails to handle non-deterministic outputs safely.
- Full-Stack Application Architecture
- Backend & API Engineering: Design high-concurrency, asynchronous backend services using Python (FastAPI) or Node.js/TypeScript. Develop robust RESTful APIs and optimize interactions using Model Context Protocol (MCP) systems where applicable.
- AI-Native Frontend Experiences: Build responsive, stateful UIs using React or Next.js. Implement streaming response interfaces (HTTP Stream), chat architectures, agentic UI interaction models, and intuitive data visualizations for complex model outputs.
- Data Management: Design relational schemas and manage transactional data workflows using databases like PostgreSQL, while pairing them seamlessly with unstructured data pipelines.
- Infrastructure
- Containerization & Deployment: Package application layers using Docker
- Cloud Architecture: Architect scalable, secure, and cost-effective solutions across cloud ecosystems like AWS or Azure
- CI/CD & Observability: Build automated deployment and test pipelines (e.g., Bitbucket & Bamboo). Implement advanced logging, error tracing, and observability tooling to monitor both typical software failures and LLM costs/latencies.
Skills for Success:
Technical Skill Matrix
- Category: Core Skills & Technologies
- AI / LLM Frameworks: LangChain, LangGraph, OpenAI/Anthropic/Gemini APIs
- Vector Databases: pgvector (PostgreSQL), Azure AI Search
- Backend Engineering:Python (FastAPI), Node.js, Java, TypeScript, API Gateway
- Frontend Engineering: React, Next.js, State Management (Zustand/Redux), SSE/WebSockets
- Databases & Storage: PostgreSQL, Redis, AWS S3 / Cloud Storage
- DevOps & Cloud: AWS, Azure, Docker, CI/CD Monitoring (OpenTelemetry)
Are you ready to say hello to BIG Possibilities?
Take the leap with Singtel to unlock new opportunities and accelerate your growth. Apply now and start your empowering career!