GenAI Fullstack Engineer - Build AI Workflows & Apps

USER EXPERIENCE RESEARCHERS PTE. LTD.

Singapore

On-site

SGD 120,000 - 150,000

Full time

13 days ago

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Job summary

USER EXPERIENCE RESEARCHERS PTE. LTD. seeks an experienced software engineer to lead end-to-end AI-enabled solutions in Singapore. You will embed with business units to shape data workflows and define measurable success criteria.

You'll prototype rapidly, build backend services with Python, FastAPI, and PostgreSQL, and design integrations across enterprise systems. You will balance speed with reliability and governance for responsible AI.

Qualifications

5+ years of software engineering experience with ownership of production applications.
Backend engineering with Python or another modern backend language, API design, asynchronous processing, integration patterns, testing, debugging, and system architecture.
Database & Data Engineering: strong experience with relational databases (prefer PostgreSQL), data modelling and query optimization; knowledge of data pipelines, search, and vector retrieval.
GenAI & Agentic Systems: hands-on experience building LLM-enabled applications, RAG solutions, tool-calling agents, or automated workflows using LangGraph, LangChain, LlamaIndex, n8n, or equivalent.
Cloud & Production Delivery: practical experience deploying and operating cloud-native applications; familiarity with AWS Bedrock and OpenSearch; Azure/GCP acceptable.
Stakeholder Partnership: strong communication and facilitation skills with non-technical users, ability to clarify needs and drive adoption.
Delivery Mindset: comfortable in ambiguous environments, balancing user value, speed, security, reliability.

Responsibilities

Embed with business units to understand workflows, pain points, data, constraints, and intended outcomes; translate needs into prioritised use cases, user stories, solution designs, and measurable success criteria.
Rapid Prototyping & Iterative Delivery: build working prototypes quickly, validate with users, and iterate based on evidence; balance speed with maintainable engineering.
Agentic Workflow Engineering: design and implement reliable AI workflows and agents using Python-based frameworks and orchestration tools such as LangGraph, LangChain, n8n, or equivalent.
Custom Application Development: build fit-for-purpose applications around business use cases, including backend services, APIs, integrations, authentication, workflow logic, and lightweight UIs.
Data & Knowledge Integration: design robust data ingestion, transformation, and retrieval pipelines; integrate enterprise systems, databases, document repositories, APIs, and search or vector services.
Backend & Database Engineering: develop secure, scalable backend services and APIs; design relational data models using Python, FastAPI, and PostgreSQL.
AI Quality, Evaluation & Observability: define evaluation datasets and acceptance criteria; test solution quality, reliability, latency, cost, and tool-call success; implement tracing and monitoring.
Security, Governance & Responsible AI: apply secure engineering practices, least-privilege access, data protection, and human oversight in line with security and responsible AI requirements.
Deployment, Adoption & Handover: own production readiness, testing, rollout, documentation, user enablement, support, and knowledge transfer.