Senior GenAI Application Engineer - Bank Sector Client

D L RESOURCES PTE LTD; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

D L RESOURCES PTE LTD is seeking a Senior GenAI Application Engineer to design, build, and deploy production-grade GenAI applications in a large-scale enterprise. You will work at the intersection of software engineering, GenAI development, enterprise integration and production delivery.

You will leverage LangGraph and LangChain, develop agentic workflows and RAG patterns, and integrate with enterprise data sources and APIs.

Qualifications

• 10+ years of software engineering experience.
• Proven production-grade GenAI applications experience.
• Hands-on with LangGraph, LangChain.
• Understanding of RAG, agentic workflows, tool calling.
• Backend skills in Python/Java.
• API design and distributed systems.
• Observability and troubleshooting for GenAI.
• Experience with enterprise integration.

Responsibilities

• Build and improve GenAI applications using LangGraph/LangChain and 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 apps with enterprise systems, APIs and data sources.
• Engineer production-grade solutions with logging, tracing, evaluation, fallback, troubleshooting.
• Bring can-do attitude and delivery mindset.

Skills

Hands-on GenAI app development

Strong engineering judgement

Strong communication skills

Delivery in complex enterprise env.

Tools

LangGraph

LangChain

Agentic workflows

RAG

Tool calling

APIs

Kubernetes/OpenShift

Job description

Responsibilities

We are looking for a Senior GenAI Application Engineer who operates effectively at the intersection of software engineering, GenAI application development, enterprise integration and production delivery.

This is not a pure research role. It is not a prompt engineering only role. The successful candidate will help design, build and improve production grade GenAI applications that are reliable, observable, maintainable and useful to real enterprise users.

1. Build and improve GenAI applications using frameworks such as LangGraph, LangChain or similar orchestration tools.
2. Work with open weight models, hosted LLMs and model serving patterns.
3. Develop agentic workflows, retrieval augmented generation, tool calling and prompt orchestration.
4. Integrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.
5. Engineer solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.
6. Bring a strong can do attitude, enthusiasm, curiosity and practical delivery mindset.

Finance or banking experience is not required. What matters more is hands on experience building real GenAI applications, strong engineering judgement and the ability to deliver in a complex enterprise environment.

What Matters Most

1. Strong hands on experience building production grade GenAI applications.
2. Practical experience with LangGraph, LangChain, agentic workflows, RAG and tool calling.
3. Understanding of open weight models and how to integrate them into real applications.
4. Ability to challenge weak designs and propose better ones.
5. Strong engineering discipline around reliability, testing, observability and maintainability.
6. Ability to work across application, data, platform, security and infrastructure teams.
7. Enthusiastic, energetic and willing to get into the details.
8. Pragmatic approach to delivery, not over engineering, not hype driven.

Requirements

Below are the key skillsets required for the role:

1. 10 or more years of software engineering experience, preferably with recent hands on experience in GenAI application development.
2. Proven experience building production grade GenAI applications, not only prototypes, experiments or demos.
3. Strong hands on experience with LangGraph, LangChain or similar orchestration frameworks.
4. Practical understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.
5. Experience integrating LLMs into real applications using APIs, backend services and enterprise data sources.
6. Experience with open weight models or strong interest backed by hands on experimentation.
7. Strong backend engineering skills using Python, Java or similar languages.
8. Good understanding of API design, distributed systems and production resilience patterns.
9. Experience with observability, logging, tracing and troubleshooting for GenAI or backend applications.
10. Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
11. Ability to write clean, maintainable and testable code.
12. Strong analytical, debugging and troubleshooting skills.
13. High ownership, high curiosity and genuine interest in building useful AI products.
14. Ability to work in a fast moving environment with incomplete information and evolving requirements.
15. Strong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.

Nice to Have

1. Experience with DeepAgent or similar agent frameworks.
2. Experience with Langfuse, Elastic or similar observability and search tools.
3. Experience with Redis for caching, conversation state, rate limiting, queue backed workflows or low latency GenAI application patterns.
4. Experience with vLLM or similar inference serving frameworks for running open weight models.
5. Experience deploying GenAI workloads on cloud or container platforms.

Key Domain/ Technical Skills

• Production GenAI Application Engineering
• LangGraph, LangChain, RAG and Agentic Workflow
• Python, Java, APIs and Enterprise Integration