New opportunity

Senior GenAI Engineer

Epergne Solutions · Singapore

About this role

The Senior GenAI Engineer will design, develop and implement enterprise-grade generative AI solutions integrated with Microsoft Azure and AWS in Singapore. The role calls for 5+ years of experience and focuses on scalable AI applications, LLM integration, multimodal models and AI agents.

Responsibilities

Design, deploy and maintain secure, efficient GenAI applications using Azure and AWS services. Work with business and technical teams to gather requirements and define solution architecture.
Integrate LLM APIs, multimodal models and AI agents into enterprise applications. Optimize models and workflows for performance, accuracy and scalability, and troubleshoot AI application and cloud deployment issues.
Collaborate with data scientists and machine learning engineers. Provide technical guidance, code reviews and mentoring, and stay current with GenAI and cloud technology developments.

Requirements

A bachelor’s or master’s degree in Computer Science, Engineering or a related field, or equivalent practical experience; strong experience developing GenAI applications and integrating LLM APIs.
Experience with Azure AI Services, Azure Functions and Azure Web Apps, and with AWS services such as Lambda, ECS, Fargate, DynamoDB or Bedrock. Proficiency in Python and/or Java; understanding of machine learning, deep learning, model evaluation and optimization; and experience with AI agents and multimodal models.
Strong problem-solving, analytical, communication and collaboration skills, with the ability to work in a fast-paced environment.

Preferred

Experience with Google Cloud AI services and cloud-based AI deployment; knowledge of Docker, Kubernetes and containerized deployment; and familiarity with CI/CD and DevOps practices. Open-source AI or cloud integration contributions are advantageous.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILarge language modelsLLM API integrationAI agentsMultimodal AIMicrosoft AzureAzure AI ServicesAzure FunctionsAzure Web AppsAmazon Web ServicesAWS LambdaAmazon ECSAWS FargateAmazon DynamoDBAmazon BedrockPythonJavaMachine learningDeep learningModel evaluationModel optimizationSolution architectureProblem-solvingAnalytical skillsCommunicationCollaborationTechnical mentoringCode reviewGoogle Cloud AIDocker beknows?

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise GenAI architecture
The role designs and deploys secure, efficient, scalable GenAI applications on Azure and AWS. Prepare to explain architecture decisions, deployment choices and how you would maintain an application after launch.
LLM, agent and multimodal integration
The posting calls for integrating LLM APIs, AI agents and multimodal models into enterprise applications. Prepare to walk through integration boundaries, workflow design and how you would evaluate the resulting behavior.
Performance and reliability
The role optimizes models and workflows for performance, accuracy and scalability, and troubleshoots application and cloud deployment issues. Prepare concrete approaches to measuring results, diagnosing failures and deciding what to improve first.
Cross-functional technical leadership
The engineer gathers requirements with business and technical teams, works with data scientists and ML engineers, and provides code reviews and mentoring. Prepare to explain how you turn requirements into architecture decisions and communicate technical feedback.

Likely challenges to think through

Balancing quality against scale
A likely challenge is improving accuracy without making an LLM or agent workflow too slow or resource-intensive. Prepare a reasoned evaluation approach with measures for quality, performance and scalability, plus examples of trade-offs you would test.
Keeping cloud integrations maintainable
Because the role spans Azure and AWS, service choices may complicate deployment and troubleshooting. Prepare to compare appropriate service patterns, explain integration boundaries and describe how you would isolate an application issue from a cloud deployment issue.
Reconciling stakeholder needs
Business requirements and technical constraints may pull an architecture in different directions. Prepare to show how you would clarify priorities, present options and reach a decision with business teams, engineers and ML specialists.

Practice your answers

Technical practice

How would you turn business requirements into an enterprise GenAI architecture?

What to coverThe role includes gathering requirements with business and technical teams and defining solution architecture. Suggested preparation: explain how you would clarify use cases, data constraints and success criteria before choosing components and documenting trade-offs.

How would you deploy a GenAI application using Azure services?

What to coverThe posting names Azure AI Services, Azure Functions and Azure Web Apps. Suggested preparation: explain where each could fit, how application components would interact, and what would influence your deployment choice.

How would you choose among the AWS services named for this role?

What to coverThe posting names Lambda, ECS, Fargate, DynamoDB and Bedrock as relevant AWS experience. Suggested preparation: compare their possible roles in an AI application and discuss workload, operational and scaling trade-offs without assuming all are needed.

How would you integrate an LLM API into a Python or Java application?

What to coverLLM API integration and proficiency in Python and/or Java are stated requirements. Suggested preparation: cover request and response handling, failure paths, testing, and how you would keep the integration maintainable.

How would you design an AI agent for an enterprise application?

What to coverBuilding AI agents is an explicit part of the role. Suggested preparation: describe how you would define the agent’s scope, control its actions, handle failures and evaluate whether an agent is preferable to a simpler workflow.

What would you assess before integrating a multimodal model?

What to coverThe posting calls for experience working with multimodal AI models and integrating them into enterprise applications. Suggested preparation: discuss input and output handling, quality checks, latency and the implications of using more than one modality.

How would you evaluate and optimize a GenAI workflow?

What to coverThe role calls for model evaluation and optimization across performance, accuracy and scalability. Suggested preparation: define measurements tied to the use case, explain how you would compare changes, and address trade-offs when one measure improves at another’s expense.

How would you keep a GenAI application secure, efficient and scalable?

What to coverBuilding and maintaining applications with all three properties is a stated responsibility. Suggested preparation: explain how you would identify risks and bottlenecks, select controls and capacity approaches, and verify their effects.

How would you isolate an AI application issue from a cloud deployment issue?

What to coverThe role includes troubleshooting both AI applications and cloud deployments. Suggested preparation: walk through a diagnostic sequence using symptoms, logs and targeted checks, then explain how you would validate the fix.

What would you consider for containerized deployment and CI/CD?

What to coverDocker, Kubernetes, containerized deployment and CI/CD or DevOps practices appear as preferred skills, not core requirements. Suggested preparation: compare when containerization would be useful and describe how testing and deployment checks could reduce release risk.

Behavioural practice

Tell me about a time you reconciled different business and technical requirements.

What to coverThe role involves working with both groups to define solution architecture. Suggested preparation: use a real situation, your actions to clarify priorities and trade-offs, and the resulting decision or outcome.

Describe a collaboration with data scientists or machine learning engineers.

What to coverThe posting calls for close collaboration with these colleagues to enhance AI capabilities. Suggested preparation: give a real example with the shared problem, your specific contribution and the result.

Tell me about a time a code review or mentoring changed an engineering outcome.

What to coverTechnical guidance, code reviews and mentoring are stated responsibilities. Suggested preparation: describe the situation, how you gave actionable guidance, and what changed as a result.

Describe how you handled a difficult production or deployment problem.

What to coverTroubleshooting AI applications and cloud deployments is part of the role. Suggested preparation: use a real example covering the symptoms, your diagnostic and communication actions, and the resolution or lesson learned.

Tell me about a time priorities shifted during a collaborative project.

What to coverThe posting calls for effectiveness in a fast-paced, collaborative environment. Suggested preparation: explain the situation, how you reprioritized and kept others aligned, and the outcome.

Sharpen your knowledge

Topics to prepare

Enterprise GenAI solution architecture
The role calls for secure, efficient, scalable applications and requirements gathering with business and technical teams. Suggested exercise: sketch an architecture from a sample business request and explain its security, scalability and performance trade-offs.
LLM evaluation and optimization
The posting requires LLM API integration and optimization for accuracy, performance and scalability. Suggested exercise: define evaluation criteria for an LLM-backed workflow, then explain how you would investigate a drop in accuracy or a rise in response time.
AI agent and multimodal design
Experience building AI agents and working with multimodal models is explicitly required. Suggested exercise: diagram an agent workflow that handles more than one input type, including where failures would be detected and handled.
Azure and AWS service selection
The posting names Azure AI Services, Functions and Web Apps, plus AWS Lambda, ECS, Fargate, DynamoDB and Bedrock. Suggested exercise: compare where the named services could fit in an application design and justify the choices you make; no particular combination is specified.
Machine learning and deep learning fundamentals
The requirements explicitly include understanding model evaluation and optimization. Suggested exercise: explain how you would compare two model approaches using evaluation results and decide what to optimize next.

Skills to revise

Implement an LLM API integration
Strong GenAI development and LLM API integration experience, with Python and/or Java proficiency, is requested. Suggested exercise: write a small integration in Python or Java that handles an API response, errors and a basic evaluation case.
Plan an Azure deployment
Azure AI Services, Azure Functions and Azure Web Apps are named requirements. Suggested exercise: outline how an LLM-backed application could be deployed using relevant named services, then trace how you would diagnose a failed request.
Plan an AWS deployment
The posting requests experience with AWS services such as Lambda, ECS, Fargate, DynamoDB and Bedrock. Suggested exercise: choose appropriate services from that list for a sample GenAI workflow and walk through a deployment or runtime failure.
Diagnose and improve a GenAI workflow
Troubleshooting cloud deployments and optimizing accuracy, performance and scalability are stated responsibilities. Suggested exercise: take a hypothetical slow or inaccurate workflow, list the evidence you would collect and prioritize corrective changes.
Review and explain technical decisions
The role includes solution-architecture discussions, code reviews, technical guidance and mentoring. Suggested exercise: review a short GenAI integration design or code sample, identify two concrete risks and explain your recommendations to both engineers and business stakeholders.

Your preparation checklist

Map your evidence to the requirements
The posting specifies 5+ years, a relevant degree or equivalent practical experience, Python and/or Java, and GenAI application development. Prepare a one-page evidence map with specific examples for each requirement; leave gaps visible rather than claiming experience you cannot substantiate.
Build an enterprise GenAI project walkthrough
The role involves LLM API integration and scalable, secure applications. Prepare a concise walkthrough of a relevant project, if available, covering the requirements, your decisions, deployment, measured results and trade-offs.
Practice an Azure and AWS design exercise
Sketch a hypothetical GenAI application using appropriate services from the posting, such as Azure AI Services and Azure Functions or AWS Bedrock and Lambda. Explain service choices, integration, security, scaling and what you would clarify before implementation.
Prepare an evaluation and optimization plan
The posting calls for multimodal models, AI agents, and optimization for accuracy, performance and scalability. For a hypothetical use case, define evaluation criteria, test cases, failure modes and how you would compare changes.
Rehearse a troubleshooting walkthrough
Prepare a step-by-step account of how you would isolate an LLM application or cloud deployment issue, from symptoms and diagnostics through a fix and verification. Distinguish any real example from your proposed approach.
Select collaboration and leadership examples
The role includes gathering requirements, working with data scientists and ML engineers, code reviews and mentoring. Prepare specific examples, if available, showing your contribution, technical guidance and outcome.
Draft questions about the role’s priorities
Ask which GenAI use cases and Azure or AWS services matter most, how the team evaluates model quality, and how engineering works with business and ML teams. You can also ask whether the listed preferred skills—such as containers, CI/CD or Google Cloud—are relevant to near-term work.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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