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

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

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About Us: Build Your Career in Banking and Technology - Contract & Permanent Roles Available

D L Resources provides Technology Managed Services and outsourced staffing for banks, multi-national corporations MNCs, technology firms, and corporates. We also offer recruitment, direct placement, and temporary contract jobs across a wide range of roles-covering both technology and business functions.

Interested Candidate May Also Reach Out Directly Via Mobile/Whatsapp +65 8833 0192 to our recruiter Edwin (EA License No. 24C2333EA Personnel No. R24123520)