- Singapore
D L Resources Pte Ltd Hiring! Full Time Software Quality Assurance Engineer – GenAI - LLM in , Earn up to SGD 9,000 - Ricebowl
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Job Description
Responsibilities
Primary Focus:
Software Quality Assurance / Software Testing / Test Automation – GenAI, LLM & Agentic AI
Secondary Exposure:
Solution Analysis / Technology Solution Design / Enterprise Integration
Domain / Project:
Global Markets, Capital Markets Banking Technology & Market Risk Technology
Role Overview
We are looking for a
Senior GenAI Quality Engineer / Solution Analyst
to design, analyse, test and validate
production-grade Generative AI (GenAI), Large Language Model (LLM), RAG and Agentic AI applications
within a complex enterprise environment.
This is
not a traditional manual QA or software testing role
.
The role combines:
Software Quality Engineering
GenAI / LLM Testing & Evaluation
Agentic AI / AI Agent Testing
UI & API Testing
Test Automation
Solution Analysis
Enterprise Integration Testing
Observability & Troubleshooting
You will work across
discovery, solution design, development, testing and release
, translating business requirements into clear application behaviours and validating end-to-end application quality across user interfaces, APIs, data flows, LLMs, RAG components, AI agents and enterprise integrations.
Key Responsibilities
GenAI / LLM Quality Engineering
Define and execute
end-to-end quality engineering and test strategies
covering:
Web / UI workflows
REST APIs
Backend services
Enterprise integrations
GenAI applications
LLM workflows
RAG pipelines
Agentic AI / AI Agent interfaces
Perform
GenAI / LLM testing and evaluation
covering:
Response quality
Task completion
Grounding
Faithfulness
Relevance
Consistency
Citation accuracy
Hallucination risk
Safe failure behaviour
Test non-deterministic / probabilistic AI systems using:
Evaluation datasets
Repeat testing
Quality thresholds
Acceptance criteria
Regression evaluation
Validate
RAG / Retrieval-Augmented Generation
solutions, including retrieval quality, grounding and response accuracy.
Agentic AI / AI Agent Testing
Test end-to-end
Agentic AI and AI Agent workflows
, including:
Multi-turn conversations
Context handling
Agent planning
Tool selection
Tool calling / function calling
Tool inputs and outputs
State transitions
Memory and state
Human-in-the-loop approvals
Handoffs
Retries
Timeouts
Fallback behaviour
Error recovery
Termination conditions
Partial failures
Validate that AI agents behave correctly across both successful and failure scenarios.
Software & API Quality Engineering
Perform:
Functional Testing
Integration Testing
API Testing
Regression Testing
Exploratory Testing
Negative Testing
Resilience Testing
Basic Performance Testing
End-to-End Testing
Design comprehensive
REST API tests
covering:
API contracts
Authentication
Authorisation
Input validation
Error handling
Idempotency
Rate limits
Downstream system failures
Test web application behaviour across browsers and realistic end-user journeys, including:
Loading states
Interrupted sessions
Error messages
Feedback capture
Accessibility fundamentals
Test Automation
Develop and maintain
risk-based test automation
that reduces:
Regression testing time
Manual testing effort
Release cycle time
Production risk
Use automation frameworks and tools such as:
Playwright
Cypress
Selenium
pytest
REST Assured
Postman
Equivalent UI / API automation frameworks
Apply pragmatic automation principles by prioritising stable, high-value and frequently executed test scenarios.
GenAI Evaluation & AI Safety Testing
Validate LLM and GenAI applications for:
Grounded responses
Hallucinations
Retrieval quality
Citation accuracy
Prompt behaviour
Prompt injection
Unsupported requests
Restricted content handling
Safe failure behaviour
Adversarial scenarios
Support
AI evaluation / LLM evaluation
using appropriate evaluation datasets, quality metrics and repeatable evaluation approaches.
Exposure to
AI Red Teaming / Adversarial Testing
would be advantageous.
Observability & Troubleshooting
Use application and GenAI observability to identify the source of defects across:
Application
LLM / Model
RAG / Retrieval
Data
API / Integration
Platform
Analyse:
Logs
Distributed traces
API requests / responses
Payloads
Network calls
Database records
Agent execution traces
Exposure to observability and LLM evaluation tools such as:
Langfuse
LangSmith
OpenTelemetry
Elastic / Elasticsearch
Splunk
is advantageous.
Solution Analysis & Design
The role also acts as a hands-on
Solution Analyst
for GenAI applications.
Responsibilities include:
Partner with product owners, business users, architects, engineers and GenAI specialists during discovery and solution design.
Analyse proposed GenAI use cases and determine whether the requirement should use:
Conventional application logic
Deterministic business rules
Search / retrieval
RAG
Workflow automation
Agentic AI
Human approval
Translate business requirements into:
Functional requirements
End-to-end solution flows
User journeys
Acceptance criteria
Interface behaviour
Decision rules
Non-functional requirements
Map interactions across:
User Interfaces
APIs
LLMs / Models
Prompts
RAG / Retrieval components
Enterprise data sources
AI Agent tools
Downstream enterprise systems
Analyse solution design trade-offs involving:
Quality
Complexity
Cost
Latency
Security
Data access
Maintainability
Operational risk
Identify missing controls, integration assumptions, ownership gaps, failure scenarios and operational risks before development begins.
Support the design of:
Human-in-the-loop approval
Fallback flows
Escalation
Exception handling
Solution Documentation
Produce practical technical and functional artefacts including:
Process Flows
Sequence Diagrams
Context Diagrams
Interface Specifications
Decision Tables
User Stories
Acceptance Criteria
Test Scenarios
Traceability Documentation
Maintain traceability across:
Business Requirement → Solution Design → Implementation → Test / Evaluation Scenario → Release Evidence
Release Quality & Governance
Create and maintain:
Test scenarios
Test datasets
Reusable regression scenarios
Test evidence
Defect reports
Quality metrics
Release quality reports
Provide evidence-based release recommendations identifying:
Known defects
Known limitations
Residual risks
Quality concerns
Areas requiring production monitoring
Core Requirements
Experience
5–8 years of experience
in Software Quality Engineering, Test Engineering, Test Automation, SDET or similar hands-on software testing roles.
Strong experience testing complex enterprise applications.
Strong experience testing:
Web applications
REST APIs
Backend services
Enterprise integrations
Test Automation / Programming
Hands-on experience with one or more of:
Playwright
Cypress
Selenium
pytest
REST Assured
Postman
Equivalent automation frameworks
Working programming knowledge of:
Python
Java
JavaScript
TypeScript
Candidates should be capable of developing, reviewing and troubleshooting test automation.
Software Engineering / DevOps
Experience with:
Git
Pull Requests
CI/CD
Automated Testing
Test Reporting
Defect Management
Experience validating distributed systems including:
Asynchronous Processing
Queues
Batch Processing
APIs
Downstream Dependencies
Enterprise Integrations
GenAI / LLM Requirements
Practical understanding of:
Generative AI / GenAI
Large Language Models / LLM
LLM Evaluation
LLM Testing
Retrieval-Augmented Generation / RAG
RAG Evaluation
Agentic AI
AI Agents
Multi-Agent Workflows
Prompts / Prompt Engineering
Context Windows
Embeddings
Tool Calling
Agent Memory & State
LLM Observability
Candidates should understand how GenAI applications differ from conventional deterministic software and how to validate probabilistic AI behaviour.
Security & Risk Testing
Understanding of software and GenAI security fundamentals including:
Access Control
Authentication / Authorisation
Sensitive Data Handling
Input Validation
Auditability
Prompt Injection
AI Safety Testing
Adversarial Testing
Nice to Have
Experience with:
Banking / Financial Services
Regulated enterprise environments
Contract Testing
Service Virtualisation
Synthetic Monitoring
Performance Testing
AI Red Teaming
Accessibility Testing / WCAG
Kubernetes
OpenShift
AWS
Containerised Application Deployment
Key Domain / Technical Skills
1. Software Quality Engineering, API Testing & Test Automation
2. GenAI / LLM Evaluation, RAG & Agentic AI Testing
3. Solution Analysis, Observability & Enterprise Integration
Key Search Keywords
GenAI Quality Engineer,AI Quality Engineer,LLM Quality Engineer,Generative AI Testing,GenAI Testing,LLM Testing,LLM Evaluation,AI Evaluation,Agentic AI Testing,AI Agent Testing,RAG Testing,RAG Evaluation,Retrieval-Augmented Generation,Software Quality Engineering,Quality Engineering,Software QA,Test Automation,SDET,Automation Testing,API Testing,REST API Testing,UI Testing,Integration Testing,Regression Testing,End-to-End Testing,Playwright,Cypress,Selenium,pytest,REST Assured,Postman,Python,Java,JavaScript,TypeScript,CI/CD,Git,Prompt Testing,Prompt Injection,Hallucination Testing,Grounding,Faithfulness,AI Safety Testing,Adversarial Testing,AI Red Teaming,Langfuse,LangSmith,OpenTelemetry,Elastic,Splunk,Observability,Distributed Systems,Kubernetes,OpenShift,Solution Analysis
About Us
: Build Your Career in Banking, Technology & Corporate Job Opportunities — Contract & Permanent Roles Available
D L Resources
provides technology managed services and outsourced staffing workforce solutions for financial services institutions, banks, multi-national corporations MNCs and technology firms. We also offer recruitment, head-hunting, direct placement, and temporary contract jobs across a wide range of roles—covering both technology and business functions.
EA License No: 24C2333
LinkedIn: *************
Software Quality Assurance / Software Testing / Test Automation – GenAI, LLM & Agentic AI
Secondary Exposure:
Solution Analysis / Technology Solution Design / Enterprise Integration
Domain / Project:
Global Markets, Capital Markets Banking Technology & Market Risk Technology
Role Overview
We are looking for a
Senior GenAI Quality Engineer / Solution Analyst
to design, analyse, test and validate
production-grade Generative AI (GenAI), Large Language Model (LLM), RAG and Agentic AI applications
within a complex enterprise environment.
This is
not a traditional manual QA or software testing role
.
The role combines:
Software Quality Engineering
GenAI / LLM Testing & Evaluation
Agentic AI / AI Agent Testing
UI & API Testing
Test Automation
Solution Analysis
Enterprise Integration Testing
Observability & Troubleshooting
You will work across
discovery, solution design, development, testing and release
, translating business requirements into clear application behaviours and validating end-to-end application quality across user interfaces, APIs, data flows, LLMs, RAG components, AI agents and enterprise integrations.
Key Responsibilities
GenAI / LLM Quality Engineering
Define and execute
end-to-end quality engineering and test strategies
covering:
Web / UI workflows
REST APIs
Backend services
Enterprise integrations
GenAI applications
LLM workflows
RAG pipelines
Agentic AI / AI Agent interfaces
Perform
GenAI / LLM testing and evaluation
covering:
Response quality
Task completion
Grounding
Faithfulness
Relevance
Consistency
Citation accuracy
Hallucination risk
Safe failure behaviour
Test non-deterministic / probabilistic AI systems using:
Evaluation datasets
Repeat testing
Quality thresholds
Acceptance criteria
Regression evaluation
Validate
RAG / Retrieval-Augmented Generation
solutions, including retrieval quality, grounding and response accuracy.
Agentic AI / AI Agent Testing
Test end-to-end
Agentic AI and AI Agent workflows
, including:
Multi-turn conversations
Context handling
Agent planning
Tool selection
Tool calling / function calling
Tool inputs and outputs
State transitions
Memory and state
Human-in-the-loop approvals
Handoffs
Retries
Timeouts
Fallback behaviour
Error recovery
Termination conditions
Partial failures
Validate that AI agents behave correctly across both successful and failure scenarios.
Software & API Quality Engineering
Perform:
Functional Testing
Integration Testing
API Testing
Regression Testing
Exploratory Testing
Negative Testing
Resilience Testing
Basic Performance Testing
End-to-End Testing
Design comprehensive
REST API tests
covering:
API contracts
Authentication
Authorisation
Input validation
Error handling
Idempotency
Rate limits
Downstream system failures
Test web application behaviour across browsers and realistic end-user journeys, including:
Loading states
Interrupted sessions
Error messages
Feedback capture
Accessibility fundamentals
Test Automation
Develop and maintain
risk-based test automation
that reduces:
Regression testing time
Manual testing effort
Release cycle time
Production risk
Use automation frameworks and tools such as:
Playwright
Cypress
Selenium
pytest
REST Assured
Postman
Equivalent UI / API automation frameworks
Apply pragmatic automation principles by prioritising stable, high-value and frequently executed test scenarios.
GenAI Evaluation & AI Safety Testing
Validate LLM and GenAI applications for:
Grounded responses
Hallucinations
Retrieval quality
Citation accuracy
Prompt behaviour
Prompt injection
Unsupported requests
Restricted content handling
Safe failure behaviour
Adversarial scenarios
Support
AI evaluation / LLM evaluation
using appropriate evaluation datasets, quality metrics and repeatable evaluation approaches.
Exposure to
AI Red Teaming / Adversarial Testing
would be advantageous.
Observability & Troubleshooting
Use application and GenAI observability to identify the source of defects across:
Application
LLM / Model
RAG / Retrieval
Data
API / Integration
Platform
Analyse:
Logs
Distributed traces
API requests / responses
Payloads
Network calls
Database records
Agent execution traces
Exposure to observability and LLM evaluation tools such as:
Langfuse
LangSmith
OpenTelemetry
Elastic / Elasticsearch
Splunk
is advantageous.
Solution Analysis & Design
The role also acts as a hands-on
Solution Analyst
for GenAI applications.
Responsibilities include:
Partner with product owners, business users, architects, engineers and GenAI specialists during discovery and solution design.
Analyse proposed GenAI use cases and determine whether the requirement should use:
Conventional application logic
Deterministic business rules
Search / retrieval
RAG
Workflow automation
Agentic AI
Human approval
Translate business requirements into:
Functional requirements
End-to-end solution flows
User journeys
Acceptance criteria
Interface behaviour
Decision rules
Non-functional requirements
Map interactions across:
User Interfaces
APIs
LLMs / Models
Prompts
RAG / Retrieval components
Enterprise data sources
AI Agent tools
Downstream enterprise systems
Analyse solution design trade-offs involving:
Quality
Complexity
Cost
Latency
Security
Data access
Maintainability
Operational risk
Identify missing controls, integration assumptions, ownership gaps, failure scenarios and operational risks before development begins.
Support the design of:
Human-in-the-loop approval
Fallback flows
Escalation
Exception handling
Solution Documentation
Produce practical technical and functional artefacts including:
Process Flows
Sequence Diagrams
Context Diagrams
Interface Specifications
Decision Tables
User Stories
Acceptance Criteria
Test Scenarios
Traceability Documentation
Maintain traceability across:
Business Requirement → Solution Design → Implementation → Test / Evaluation Scenario → Release Evidence
Release Quality & Governance
Create and maintain:
Test scenarios
Test datasets
Reusable regression scenarios
Test evidence
Defect reports
Quality metrics
Release quality reports
Provide evidence-based release recommendations identifying:
Known defects
Known limitations
Residual risks
Quality concerns
Areas requiring production monitoring
Core Requirements
Experience
5–8 years of experience
in Software Quality Engineering, Test Engineering, Test Automation, SDET or similar hands-on software testing roles.
Strong experience testing complex enterprise applications.
Strong experience testing:
Web applications
REST APIs
Backend services
Enterprise integrations
Test Automation / Programming
Hands-on experience with one or more of:
Playwright
Cypress
Selenium
pytest
REST Assured
Postman
Equivalent automation frameworks
Working programming knowledge of:
Python
Java
JavaScript
TypeScript
Candidates should be capable of developing, reviewing and troubleshooting test automation.
Software Engineering / DevOps
Experience with:
Git
Pull Requests
CI/CD
Automated Testing
Test Reporting
Defect Management
Experience validating distributed systems including:
Asynchronous Processing
Queues
Batch Processing
APIs
Downstream Dependencies
Enterprise Integrations
GenAI / LLM Requirements
Practical understanding of:
Generative AI / GenAI
Large Language Models / LLM
LLM Evaluation
LLM Testing
Retrieval-Augmented Generation / RAG
RAG Evaluation
Agentic AI
AI Agents
Multi-Agent Workflows
Prompts / Prompt Engineering
Context Windows
Embeddings
Tool Calling
Agent Memory & State
LLM Observability
Candidates should understand how GenAI applications differ from conventional deterministic software and how to validate probabilistic AI behaviour.
Security & Risk Testing
Understanding of software and GenAI security fundamentals including:
Access Control
Authentication / Authorisation
Sensitive Data Handling
Input Validation
Auditability
Prompt Injection
AI Safety Testing
Adversarial Testing
Nice to Have
Experience with:
Banking / Financial Services
Regulated enterprise environments
Contract Testing
Service Virtualisation
Synthetic Monitoring
Performance Testing
AI Red Teaming
Accessibility Testing / WCAG
Kubernetes
OpenShift
AWS
Containerised Application Deployment
Key Domain / Technical Skills
1. Software Quality Engineering, API Testing & Test Automation
2. GenAI / LLM Evaluation, RAG & Agentic AI Testing
3. Solution Analysis, Observability & Enterprise Integration
Key Search Keywords
GenAI Quality Engineer,AI Quality Engineer,LLM Quality Engineer,Generative AI Testing,GenAI Testing,LLM Testing,LLM Evaluation,AI Evaluation,Agentic AI Testing,AI Agent Testing,RAG Testing,RAG Evaluation,Retrieval-Augmented Generation,Software Quality Engineering,Quality Engineering,Software QA,Test Automation,SDET,Automation Testing,API Testing,REST API Testing,UI Testing,Integration Testing,Regression Testing,End-to-End Testing,Playwright,Cypress,Selenium,pytest,REST Assured,Postman,Python,Java,JavaScript,TypeScript,CI/CD,Git,Prompt Testing,Prompt Injection,Hallucination Testing,Grounding,Faithfulness,AI Safety Testing,Adversarial Testing,AI Red Teaming,Langfuse,LangSmith,OpenTelemetry,Elastic,Splunk,Observability,Distributed Systems,Kubernetes,OpenShift,Solution Analysis
About Us
: Build Your Career in Banking, Technology & Corporate Job Opportunities — Contract & Permanent Roles Available
D L Resources
provides technology managed services and outsourced staffing workforce solutions for financial services institutions, banks, multi-national corporations MNCs and technology firms. We also offer recruitment, head-hunting, direct placement, and temporary contract jobs across a wide range of roles—covering both technology and business functions.
EA License No: 24C2333
LinkedIn: *************
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