Gen AI Engineer

EAMES CONSULTING GROUP (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 150,000 - 190,000; Full time

Job summary

EAMES CONSULTING GROUP (SINGAPORE) PTE. LTD. is seeking a Senior GenAI Application Engineer to build production-grade GenAI systems at the intersection of software engineering, enterprise integration, and LLM orchestration.

You will design, implement, and operate end-to-end GenAI solutions, focusing on robust tooling, observability, and scalable deployment across enterprise environments in Singapore.

Qualifications

• 6+ years of core software engineering experience shipping GenAI applications to production.
• Proficiency in Python or Java, API design, and distributed backend resilience patterns.
• Hands-on experience with LangGraph, LangChain, RAG architectures and agentic workflows.
• Experience with observability, tracing, and logging for GenAI systems (Langfuse, Elastic).
• Familiarity with open-weight models and model-serving setups (e.g., vLLM).
• Experience with containerized deployments (Kubernetes/OpenShift) and Redis.

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.

Skills

Python

Java

API design

Distributed backend

LangGraph

LangChain

RAG

Agentic workflows

Observability

Model serving

Open-weight models

Kubernetes

OpenShift

Redis

Tools

LangGraph

LangChain

Elastic

Langfuse

vLLM

Kubernetes

OpenShift

Job description

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.