Senior GenAI Application Analyst (Contract)

Tangspac Consulting Group; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Tangspac Consulting Group is recruiting a senior GenAI engineer to build and scale production-grade GenAI applications in Singapore. You will work with LangGraph, LangChain and other orchestration tools, integrating LLMs into enterprise systems and APIs.

The role emphasizes robust telemetry, logging, tracing and fault-tolerance in production environments. You should have 5+ years of software development experience, strong back-end skills in Python/Java, and hands-on experience with open-weight

Qualifications

• 5+ years of software engineering experience.
• Proven production-grade GenAI applications experience.
• Hands-on with LangGraph/LangChain or similar orchestration frameworks.
• Understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.
• Experience integrating LLMs via APIs and enterprise data sources.
• Experience with open-weight models or active experimentation.

Responsibilities

• Build and improve GenAI applications using LangGraph, LangChain or similar orchestration tools.
• Work with open-weight models, hosted LLMs and model serving patterns.
• Develop agentic workflows, retrieval-augmented generation, tool calling and prompt orchestration.
• Integrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.
• Engineer solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.

Skills

GenAI development

Production-grade software

Python/Java

Debugging/troubleshooting

Code maintainability

Ownership/curiosity

API design

Tools

LangGraph

LangChain

Kubernetes/OpenShift

Open-weight models

vLLM

Redis

Langfuse/Elastic

Job description

Job De ion

• Build and improve GenAI applications using frameworks such as LangGraph, LangChain or similar orchestration tools.
• Work with open-weight models, hosted LLMs and model serving patterns.
• Develop agentic workflows, retrieval-augmented generation, tool calling and prompt orchestration.
• Integrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.
• Engineering solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.

Requirements

• 5 or more years of software engineering experience, preferably with recent hands-on experience in GenAI application development.
• Proven experience building production-grade GenAI applications, not only prototypes, experiments or demos.
• Strong hands on experience with LangGraph, LangChain or similar orchestration frameworks.
• Practical understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.
• Experience integrating LLMs into real applications using APIs, backend services and enterprise data sources.
• Experience with open-weight models or strong interest backed by hands‑on experimentation.
• Strong backend engineering skills using Python, Java or similar languages.
• Good understanding of API design, distributed systems and production resilience patterns.
• Experience with observability, logging, tracing and troubleshooting for GenAI or backend applications. Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
• Ability to write clean, maintainable and testable code.
• Strong analytical, debugging and troubleshooting skills.
• High ownership, high curiosity and genuine interest in building useful AI products.

Nice to Have

• Experience with DeepAgent or similar agent frameworks.
• Experience with Langfuse, Elastic or similar observability and search tools.
• Experience with Redis for caching, conversation state, rate limiting, queue‑backed workflows or low‑latency GenAI application patterns.
• Experience with vLLM or similar inference serving frameworks for running Openweight models.
• Experienced in deploying GenAI workloads on cloud or container platforms.