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Open nowPosted 29 days ago

Gen AI Engineer

MyCareersFuture94,028 open roles

Pay
SGD 11,000 – SGD 14,000 a Monthly
Where
Central, Singapore
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Your applicationOpen nowGen AI EngineerMyCareersFuture · Central, Singapore

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  2. 3.3%3 days
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  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 29 days ago

The posting

We are partnered with an established regional financial institution in Singapore that is scaling its enterprise AI engineering capabilities.

They are building out practical, high-impact generative AI applications and are looking for a Senior GenAI Application Engineer to sit at the intersection of core software engineering, enterprise integration, and LLM orchestration. This is not a prompt-tweaking or academic research role; it is an end-to-end engineering position focused on getting resilient, observable AI systems into production for enterprise users.

Key Responsibilities:

  • Architect, ship, and scale robust GenAI applications using modern orchestration frameworks (e.g., LangGraph, LangChain) and custom agentic workflows.
  • Build end-to-end Retrieval-Augmented Generation (RAG) pipelines, context management solutions, and structured tool-calling mechanisms integrated with enterprise systems and backend APIs.
  • Implement production-grade engineering standards around LLM applications, including tracing, logging, automated evaluations, and defensive fallback patterns.
  • Integrate open-weight models, hosted LLM endpoints, and specialized inference-serving patterns into core distributed architectures.
  • Collaborate closely with cross-functional data, platform, infrastructure, and security teams to drive scalable, secure deployment across enterprise environments.

Requirements:

  • 6+ years of core software engineering experience, with strong hands-on experience shipping GenAI applications into production (beyond demos, hackathons, or basic POCs).
  • Deep experience with Python or Java, API design, and distributed backend resilience patterns.
  • Demonstrated expertise in LangGraph, LangChain, RAG architectures, and agentic workflows.
  • Practical exposure to observability, tracing, and logging for GenAI systems (e.g., Langfuse, Elastic).
  • Experience with or solid interest in open-weight models and model-serving setups (e.g., vLLM).
  • Familiarity with containerized deployments (Kubernetes, OpenShift) and state/cache management (e.g., Redis).
  • Strong engineering discipline, high ownership, and the ability to challenge weak technical designs.
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