GenAI Application Engineer (LLM / RAG / Agentic AI)

D L RESOURCES PTE LTD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Bank Sector Client Located In Singapore is seeking a Senior GenAI Application Engineer to design, build and deliver production-grade GenAI/LLM applications for enterprise environments.

You will work across LLM application development, RAG, agentic AI, LangGraph/LangChain orchestration, backend services and production deployment. Strong backend fundamentals and experience moving GenAI apps to production are essential.

Qualifications

• 6+ years of software engineering experience
• Strong backend development experience using Python, Java or similar languages
• Strong understanding of REST APIs / API development
• Backend services, distributed systems and enterprise integration
• Production-grade code quality and maintainability
• Experience taking GenAI apps from POC to production

Responsibilities

• Design, develop and deploy production-grade GenAI/LLM applications for enterprise users
• Build AI Agent workflows using LangGraph/LangChain or similar orchestration frameworks
• Develop RAG solutions including retrieval workflows and prompt orchestration
• Integrate LLM applications with enterprise systems, REST APIs and databases
• Support model integration and LLM inference/model-serving architectures
• Deploy GenAI apps in containerized environments (Kubernetes/OpenShift)

Skills

GenAI applications

Python/Java backend

REST APIs

LangGraph

LangChain

RAG

AI Agents/Agent workflows

Kubernetes/OpenShift

LLM observability

Tools

LangGraph

LangChain

Kubernetes

OpenShift

vLLM

Elasticsearch

Redis

Job description

Client: Bank Sector Client Located In Singapore

LLM / RAG / Agentic AI / LangGraph / LangChain

Role Overview

We are looking for a Senior GenAI Application Engineer to design, build and deliver production-grade Generative AI (GenAI) and Large Language Model (LLM) applications for enterprise environments.

This is a hands-on GenAI application engineering / software engineering role focused on building real-world AI applications rather than pure AI research or prompt engineering.

You will work across LLM application development, Retrieval-Augmented Generation (RAG), Agentic AI, AI agents, LangGraph/LangChain orchestration, backend engineering, enterprise APIs and production deployment.

The successful candidate should have strong software engineering fundamentals and practical experience taking GenAI / LLM applications from prototype or Proof of Concept (POC) into production.

Banking or financial services experience is not required.

Key Responsibilities

• Design, develop and enhance production-grade GenAI / LLM applications used by enterprise users.
• Build Agentic AI / AI Agent workflows using frameworks such as LangGraph, LangChain or similar LLM orchestration frameworks.
• Develop Retrieval-Augmented Generation (RAG) solutions including retrieval workflows, context management and prompt orchestration.
• Build applications involving:
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • AI Agents / Agentic AI
  • Tool calling / function calling
  • Prompt orchestration
  • Context management
• Integrate LLM applications with enterprise systems, REST APIs, backend services, databases, enterprise data sources and operational platforms.
• Work with both open-weight / open-source models and hosted LLM services.
• Support model integration and LLM inference / model-serving architectures.
• Develop reliable backend services using Python, Java or similar programming languages.
• Design scalable APIs and services for enterprise GenAI applications.
• Implement production engineering practices including:
  • Logging
  • Monitoring
  • Distributed tracing
  • LLM observability
  • Evaluation / LLM evaluation
  • Error handling
  • Fallback mechanisms
  • Debugging and troubleshooting
• Deploy and support GenAI applications within containerized environments such as Kubernetes or OpenShift.
• Work closely with application, data, platform, infrastructure, DevOps, security and business teams.
• Review technical designs, identify weaknesses and recommend practical improvements.
• Ensure solutions are scalable, reliable, maintainable, observable and production-ready.

Key Requirements

Software Engineering

• 6+ years of software engineering experience, preferably with recent hands‑on experience developing GenAI / LLM applications.
• Strong backend software development experience using Python, Java or similar languages.
• Strong understanding of:
  • REST APIs / API development
  • Backend services
  • Distributed systems
  • Enterprise application integration
  • Scalability and resilience
  • Production application architecture
• Ability to write clean, maintainable and testable production code.

Generative AI / LLM Engineering

Proven hands‑on experience building and deploying production-grade Generative AI applications, beyond simple prototypes, demos or hackathons.

Strong practical knowledge of:

• Generative AI / GenAI
• Large Language Models (LLMs)
• LangGraph
• LangChain
• Retrieval-Augmented Generation (RAG)
• Agentic AI
• AI Agents / Agent workflows
• LLM orchestration
• Tool calling / function calling
• Prompt engineering / prompt orchestration
• Context management
• LLM application integration

Candidates should understand how these technologies are used together to build reliable enterprise AI applications.

Production GenAI Engineering

Experience implementing production engineering practices for GenAI or backend applications, including:

• LLM observability
• Logging and monitoring
• Distributed tracing
• LLM / application evaluation
• Failure handling
• Fallback mechanisms
• Debugging and troubleshooting
• Application reliability
• Performance and scalability

Experience taking AI applications from POC / prototype through production deployment is particularly important.

Open-Weight Models & Model Serving

Experience working with or integrating:

• Open-weight models / open-source LLMs
• Hosted LLM APIs
• Model-serving platforms
• LLM inference services

Hands‑on exposure to vLLM or similar LLM inference / model-serving frameworks would be advantageous.

Cloud / Containers / Platform Engineering

Familiarity with production deployment environments such as:

• Kubernetes
• OpenShift
• Containerized application deployment
• Cloud or enterprise infrastructure environments

Candidates should be comfortable collaborating with DevOps, platform, infrastructure and security teams when deploying GenAI applications.

Nice to Have

Experience with one or more of the following would be advantageous:

• Langfuse or similar LLM observability platforms
• Elastic / Elasticsearch
• Redis
• vLLM
• DeepAgent or similar Agentic AI frameworks
• Cloud-based GenAI deployments
• LLM model serving / inference
• Caching and conversation-state management
• Queue-backed AI workflows
• Low‑latency GenAI application architectures

What We Are Looking For

The ideal candidate is a hands‑on software engineer who has moved into Generative AI application development, rather than someone focused purely on AI research or prompt engineering.

You should be able to:

• Build real enterprise GenAI applications
• Design reliable RAG and Agentic AI architectures
• Integrate LLMs with APIs and enterprise systems
• Write production-quality backend code
• Diagnose complex technical problems
• Challenge weak technical designs
• Work across application, data, platform, infrastructure and security teams
• Balance engineering quality with pragmatic delivery

We value engineers who are curious, practical, delivery-focused and willing to work hands‑on with the technology.

Core Technical Skills / Search Keywords

Generative AI (GenAI),Large Language Models (LLM),LLM Applications,GenAI Application Engineering,Agentic AI,AI Agents,LangGraph,LangChain,Retrieval-Augmented Generation (RAG),RAG Pipelines,LLM Orchestration,Agentic Workflows,Tool Calling,Function Calling,Prompt Engineering,Prompt Orchestration,Context Management,Open-Weight Models,Open-Source LLMs,Python,Java,REST API,Backend Engineering,Enterprise Integration,Distributed Systems,LLM Observability,Langfuse,Elastic,Redis,vLLM,Kubernetes,OpenShift,Model Serving,LLM Inference,Logging,Tracing,LLM Evaluation

Key Domain / Technical Skills

1. Production GenAI / LLM Application Engineering
2. LangGraph, LangChain, RAG & Agentic AI Workflows
3. Python / Java, APIs, Backend Engineering & Enterprise Integration

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