New opportunity

Senior Full Stack Engineer

Johnson & Johnson Services, Inc. · Singapore

About this role

The Senior Full Stack Engineer will join the TS APAC Digital and Connected Commerce team at Johnson & Johnson to build scalable, AI-powered platforms that connect healthcare professionals with J&J products. This hands-on role focuses on delivering modern digital healthcare solutions by integrating Generative AI into both product features and the development workflow. Responsibilities include designing and maintaining scalable web applications, developing APIs, managing data models, and deploying applications in AWS. You will integrate LLM APIs and orchestration frameworks like LangChain or LangGraph to build AI-driven features such as search, summarization, and conversational interfaces. A core aspect of this role is shaping AI-native engineering practices, including AI-paired programming, spec-driven development, and using AI for testing and code reviews. Requirements include 4–8 years of experience in production-grade web application development, strong full stack expertise, cloud platform experience (AWS preferred), and hands-on experience with LLM integration, prompt engineering, and AI-assisted coding tools. Preferred qualifications include experience with real-time systems, containerization (Docker, Kubernetes), and CI/CD automation. You will operate in a fast-paced, collaborative Agile environment, partnering with cross-functional stakeholders to prototype and scale impactful solutions that improve patient outcomes.

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

Skills for this role

Node.jsTypeScriptReactPythonRESTful APIsGraphQLSQLNoSQLAWSServerlessMicroservicesContainerizationLLM APIsOpenAIAnthropicBedrockLangChainLangGraphGitHub CopilotAmazon KiroPrompt EngineeringGitCI/CDAgileWebSocketsStreaming APIs

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

AI-Native Development Workflow Integration
The role requires shifting beyond traditional coding to actively shaping AI-native practices. You must demonstrate proficiency in AI-paired programming, spec-driven development, and using LLMs for automated testing and code reviews to improve team velocity and code quality.
Scalable AI-Powered Platform Engineering
You are expected to design and maintain scalable web applications on AWS that integrate LLM APIs and orchestration frameworks like LangChain or LangGraph. This involves building production-grade features such as conversational interfaces, search, and summarization while managing complex data models.
Cross-Functional Agile Collaboration
Operating in a fast-paced Agile environment, you must partner with product, design, and business stakeholders to translate requirements into technical solutions. This requires the ability to rapidly prototype, iterate, and scale solutions that connect healthcare professionals with J&J products.

Likely challenges to think through

Balancing Innovation with Enterprise Standards
The challenge lies in maintaining an experimentation mindset for AI-first solutions while adhering to the rigorous reliability, security, and scalability requirements of a global healthcare enterprise. Prepare by discussing how you implement quality gates and human validation when using AI-generated code.
Managing Non-Deterministic AI Outputs
Integrating LLMs into production systems introduces non-determinism that can impact application stability. You should prepare to discuss your experience with prompt engineering, output evaluation, and building robust error-handling mechanisms within orchestration frameworks to ensure consistent user experiences.

Practice your answers

Technical practice

Integrating LLMs into Production Applications

What to coverDiscuss the architectural considerations when integrating LLM APIs like OpenAI or Bedrock into a scalable web application. Address how you handle latency, rate limiting, and cost management while ensuring reliable, production-grade performance.

Orchestration with LangChain or LangGraph

What to coverExplain a scenario where you would choose LangGraph over LangChain for building an AI-driven feature. Focus on state management, cyclic workflows, and how these frameworks help in building complex conversational interfaces.

Spec-Driven Development and AI

What to coverDescribe your approach to spec-driven development when using AI-assisted coding tools. Explain how you translate product requirements into structured schemas or prompts to ensure the AI generates accurate, maintainable code that meets quality gates.

Scalable Data Modeling for AI Features

What to coverCompare the use of SQL and NoSQL databases in the context of AI-powered platforms. Discuss how you would structure data to support features like search, summarization, or recommendation engines while maintaining system scalability.

Real-Time Systems and Streaming APIs

What to coverHow do you implement real-time updates or streaming interfaces in a React/Node.js application? Discuss the trade-offs between WebSockets and Server-Sent Events (SSE) when delivering AI-generated content to the user.

AI-Assisted Testing and Code Quality

What to coverHow do you validate code generated by AI tools like GitHub Copilot? Explain your strategy for integrating AI-generated unit and integration tests into a CI/CD pipeline while maintaining human oversight and code quality standards.

Cloud Deployment and Observability on AWS

What to coverDetail your process for deploying and monitoring a microservices-based application on AWS. Focus on how you ensure observability for AI-integrated services and manage containerized environments using Docker or Kubernetes.

Prompt Engineering and Output Evaluation

What to coverExplain your methodology for evaluating the quality and consistency of LLM outputs. Suggest how you would implement a feedback loop or evaluation framework to refine prompts and improve the accuracy of AI-driven features over time.

Behavioural practice

Driving AI-Native Engineering Practices

What to coverDescribe a situation where you introduced a new development practice or tool to your team to improve productivity. How did you gain buy-in from stakeholders, and what metrics did you use to measure the impact on delivery speed or code quality?

Collaborating in a Cross-Functional Agile Environment

What to coverProvide an example of a time you had to partner with non-technical stakeholders to prototype a complex solution. How did you translate business requirements into technical specifications while managing expectations during the rapid iteration process?

Handling Technical Ambiguity in Experimentation

What to coverTell me about a project where you had to pivot your approach due to technical limitations or changing requirements. How did you maintain an experimentation mindset while ensuring the final solution was production-ready?

Mentoring and Fostering a Culture of Learning

What to coverDescribe how you have contributed to a culture of continuous learning within your team, particularly regarding emerging technologies like Generative AI. How do you balance the need for rapid delivery with the time required for team experimentation and skill development?

Sharpen your knowledge

Topics to prepare

AI-Native Engineering Workflows
The role emphasizes integrating AI into the development lifecycle, specifically spec-driven development and AI-paired programming. Exercise: Document a recent feature implementation by creating a structured OpenAPI schema or detailed functional specification, then simulate how you would use an LLM to generate the initial boilerplate code and unit tests from that specification.
LLM Orchestration Patterns
The position requires building AI-driven features using frameworks like LangChain or LangGraph. Exercise: Design a conceptual architecture for a RAG (Retrieval-Augmented Generation) system that summarizes healthcare documentation, focusing on how you would manage state and tool-calling logic within a LangGraph workflow.

Skills to revise

Prompt Engineering and Evaluation
You must demonstrate the ability to refine LLM outputs for production-grade applications. Exercise: Create a set of test cases for a prompt designed to extract structured data from unstructured clinical text, and define a metric or human-in-the-loop validation process to evaluate the accuracy and consistency of the model's responses.
Full Stack Cloud Deployment (AWS)
The role requires deploying scalable applications on AWS using modern architectures. Exercise: Outline a CI/CD pipeline strategy for a Node.js/TypeScript application that includes automated testing, containerization with Docker, and deployment to an AWS serverless or container-based environment.
Real-time System Integration
The role values experience with streaming APIs and real-time updates. Exercise: Review your experience with WebSockets or Server-Sent Events (SSE) and prepare to explain how you would handle connection state, message serialization, and performance bottlenecks in a high-concurrency healthcare dashboard.

Your preparation checklist

Develop an AI-integrated project portfolio
Build a functional web application using Node.js or Python that integrates an LLM via LangChain or LangGraph. Ensure the project demonstrates real-world utility, such as a summarization or conversational interface, and document how you managed the data model and API integration.
Refine your AI-native development workflow
Prepare to discuss your specific experience using tools like GitHub Copilot or Amazon Q (Kiro) for code generation, testing, and debugging. Be ready to explain your process for 'spec-driven development' and how you maintain quality gates when using AI to generate code.
Prepare an AWS and architecture case study
Review your experience deploying scalable applications on AWS. Prepare to explain your approach to serverless or containerized (Docker/Kubernetes) architectures, focusing on how you ensure reliability, observability, and performance in a production environment.
Draft questions regarding AI-native engineering culture
Formulate thoughtful questions about how the team balances rapid AI experimentation with enterprise-grade security and compliance. Ask about their current 'human-in-the-loop' validation processes for AI-generated code and how they measure developer productivity improvements.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…