New opportunity

LangChain and Python Developer – Singapore

Pan Asia Group · Singapore

About this role

Develop, optimize, and deploy applications using large language models (LLMs), LangChain, and retrieval-augmented generation (RAG) in Singapore.

Responsibilities

Design end-to-end Python applications with LangChain to orchestrate LLM calls, agents, chains, and custom tools.
Build and optimize RAG data pipelines for ingestion, chunking, embedding generation, and vector database indexing to improve context relevance and performance.
Work with LLMs such as OpenAI, Claude, and Llama; apply prompt engineering and fine-tuning strategies to achieve desired application behavior.
Deploy and maintain production LLM applications, balancing performance, latency, and cost. Develop metrics and evaluation frameworks to test and monitor output quality and reliability.

Required qualifications

Bachelor’s degree in Computer Science, Data Science, or a related field.
3+ years of professional Python development experience and 1+ years of hands-on experience with LangChain or a similar LLM orchestration framework.
Experience with embedding models and vector databases such as Pinecone, Chroma, or Milvus.
Solid understanding of AWS, Azure, or GCP for application deployment.

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

Skills for this role

PythonLangChainLarge Language ModelsRetrieval-Augmented GenerationAI agentsPrompt engineeringFine-tuningData pipelinesEmbeddingsVector databasesPineconeChromaMilvusOpenAIClaudeLlamaAWSAzureGCPLLM evaluation

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

End-to-end LLM application development
The role calls for designing and implementing Python applications with LangChain to orchestrate LLM calls, agents, chains, and custom tools.
RAG pipeline quality
The posting emphasizes ingestion, chunking, embedding generation, and vector database indexing to improve retrieval performance and context relevance.
Model behavior
The role involves working with models such as OpenAI, Claude, and Llama, using prompt engineering and fine-tuning strategies to achieve desired application behavior.
Production deployment
The developer is expected to deploy and maintain LLM applications, balancing performance, latency, and cost. The posting also requires an understanding of AWS, Azure, or GCP deployment.
Output evaluation
The role calls for metrics and evaluation frameworks to test and monitor the quality and reliability of LLM outputs.

Likely challenges to think through

Retrieval relevance versus pipeline performance
A likely trade-off is that chunking, embeddings, and indexing choices can affect both the context retrieved and the work required to retrieve it. Prepare to explain how you would compare retrieval approaches using relevance and performance measures; this is a preparation hypothesis, not a reported employer problem.
Production quality versus latency and cost
The posting explicitly calls for high performance, low latency, and cost constraints. A likely difficulty is deciding when a more complex LLM workflow is worth its added latency or expense. Prepare to describe how you would measure those trade-offs before and after deployment.
Improving behavior without losing reliability
Prompt engineering and fine-tuning aim to shape behavior, while the role also requires output evaluation. A likely challenge is showing that a change improves desired behavior without degrading other outputs. Prepare to explain a before-and-after evaluation approach; no specific company evaluation process is stated.

Practice your answers

Technical practice

Suggested practice: How would you design an end-to-end Python application using LangChain for an LLM-backed task?

What to coverThe posting calls for Python applications that orchestrate LLM calls, chains, agents and custom tools. Explain how you would divide responsibilities among those components, handle failures and keep the application maintainable.

Suggested practice: When would you use an agent rather than a fixed chain?

What to coverThe role includes both agents and chains. Compare predictable execution with flexible tool selection, and explain how you would limit unnecessary calls or unreliable tool use.

Suggested practice: How would you build an ingestion pipeline for a RAG system?

What to coverThe posting identifies ingestion as part of RAG engineering. Walk through preparing source content, tracking changes and checking that indexed content remains usable for retrieval.

Suggested practice: How would you choose and evaluate a chunking strategy?

What to coverChunking is an explicit responsibility. Discuss the trade-off between preserving context and retrieving focused passages, then describe how you would test its effect on answer relevance.

Suggested practice: How do embedding models and vector indexing affect retrieval?

What to coverThe role requires experience with embedding models and vector databases such as Pinecone, Chroma or Milvus. Explain how embedding choice and indexing decisions affect relevance, update handling and performance without assuming a particular database is used.

Suggested practice: How would you diagnose a RAG application that retrieves irrelevant context?

What to coverImproving context relevance is a stated goal. Describe how you would isolate ingestion, chunking, embeddings and retrieval as possible causes, using example queries and measurable results to guide changes.

Suggested practice: When would you change a prompt, and when would you consider fine-tuning?

What to coverThe posting mentions both prompt engineering and fine-tuning to achieve desired behavior. Compare their suitability, required evidence and maintenance costs, and explain how you would verify an improvement.

Suggested practice: How would you compare LLMs for this application's needs?

What to coverThe posting names OpenAI, Claude and Llama as examples of models the role may work with. Suggest evaluating output quality, reliability, latency and cost against the application's intended behavior rather than assuming one model is best.

Suggested practice: How would you deploy and maintain a production LLM application on a cloud platform?

What to coverThe role calls for production deployment and understanding of AWS, Azure or GCP. Discuss application configuration, operational monitoring and ways to manage latency and cost without presuming a specific cloud architecture.

Suggested practice: What evaluation framework would you use to monitor LLM output quality?

What to coverDeveloping metrics and evaluation frameworks for quality and reliability is an explicit duty. Explain how you would assemble representative test cases, measure answer quality and retrieval performance, and use ongoing results to detect regressions.

Behavioural practice

Suggested practice: Tell me about a time you improved the relevance of retrieved context.

What to coverUse a real example, if you have one, and set out the situation, the change you made to the pipeline and the result you observed. The posting's RAG responsibilities make it useful to explain how you distinguished the cause of poor relevance from its symptoms.

Suggested practice: Describe a time you responded to a reliability problem in a deployed application.

What to coverFor a real example, cover the situation, your diagnostic and corrective actions, and the resulting behavior. Relate the example to maintaining production applications without claiming experience you do not have.

Suggested practice: Tell me about a time you had to balance output quality, latency and cost.

What to coverThe posting names all three as production concerns. Use a real situation to explain the trade-off, what you changed or recommended, and the measured result.

Suggested practice: Describe a time evaluation results changed your technical approach.

What to coverUse a real situation, the evidence you gathered, the action you took and the result. The role's evaluation responsibility makes it useful to show how you checked whether the change actually improved quality or reliability.

Sharpen your knowledge

Topics to prepare

LLM orchestration design
Sketch how a Python application would route a request through LangChain calls, chains, an agent and a custom tool; explain when each component is useful. The posting explicitly names these components in its end-to-end application work.
RAG pipeline and retrieval relevance
Diagram ingestion, chunking, embedding generation, vector indexing and retrieval; explain how changing chunk boundaries could affect context relevance. These stages are explicit RAG responsibilities.
Prompting versus fine-tuning
For two example output problems, explain whether you would first revise a prompt or consider fine-tuning, and how you would judge the result. The role calls for both strategies to achieve desired application behavior.
Production trade-offs
Walk through a hypothetical LLM request and identify where latency, performance and cost may conflict. The posting explicitly calls for maintaining production applications within those constraints.
Output quality and reliability
Define what a good answer and an unreliable answer would look like for a sample RAG question, then propose measures for both. The role includes developing evaluation metrics and monitoring LLM outputs.

Skills to revise

Build a Python–LangChain flow
As a revision exercise, implement a small Python flow that sends an LLM request through a chain and a custom tool; trace the inputs and outputs. This practices the advertised Python and LLM-orchestration work.
Create an indexed RAG pipeline
In a practice project, ingest a short document set, split it into chunks, generate embeddings and index them in one listed vector database, such as Chroma. This mirrors the pipeline stages and database experience in the posting.
Tune retrieval results
Run a few sample questions against that index, inspect the retrieved chunks, change chunking or indexing choices, and compare context relevance. This practices the stated goal of optimizing RAG performance.
Test prompt changes
Write two prompt variants for one task, run the same example inputs through a listed LLM if available, and record differences in desired behavior. This exercises the prompt-engineering responsibility without assuming fine-tuning is required for every task.
Evaluate and monitor a small application
Create a short set of test questions and expected answer criteria; record output quality, failures, response time and approximate cost across runs. This practices the posting’s evaluation and production trade-offs.
Rehearse cloud deployment
As a preparation exercise, deploy the practice application on one of AWS, Azure or GCP, then check that it runs and measure response time. The qualification calls for cloud-deployment understanding; this exercise is a suggestion, not a stated deployment requirement.

Your preparation checklist

Map your evidence to the requirements
Prepare a one-page summary of relevant education, professional Python work, hands-on LangChain or similar framework work, embedding and vector database experience, and cloud deployment knowledge. Include dates and your specific contributions where applicable; the posting specifies a related bachelor’s degree, 3+ years of Python, and 1+ years with an orchestration framework.
Prepare an end-to-end application walkthrough
Select a relevant project, if available, and diagram its Python architecture. Be ready to explain how LLM calls, chains, agents, or custom tools were orchestrated, what you implemented, and the trade-offs you made.
Practice a RAG pipeline design exercise
Draft a design covering ingestion, chunking, embedding generation, vector indexing, and retrieval. Explain how you would test context relevance and performance; these stages are explicit responsibilities in the posting.
Show how you would shape model behavior
Prepare a short example comparing prompt approaches and explaining when fine-tuning might be appropriate. Use OpenAI, Claude, or Llama only where you can discuss relevant work or a clearly hypothetical approach.
Plan production deployment and evaluation
Sketch deployment on one of AWS, Azure, or GCP, including how you would monitor output quality and reliability while managing latency, performance, and cost. Label the architecture as a proposed design unless it reflects work you actually did.
Write focused questions for Pan Asia Group
Prepare questions about the RAG data sources, current orchestration and vector database choices, evaluation criteria, and deployment constraints. These are questions to clarify the role, not established details of the employer’s systems.

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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