# LangChain Developer at KDCI

> Hybrid · Philippines · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/recruitee%3Akdci1%3A2682716 (read 2026-11-08).

## Facts (observed)

- Company: [KDCI](https://alderhq.app/companies/kdci)
- Location text: Pasig City, National Capital Region, Philippines
- Where it can be done from (inferred): Hybrid · Philippines (evidence: "Location: Pasig City, National Capital Region, Philippines")
- Posted: 2026-07-20 (111 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://kdci1.recruitee.com/o/langchain-developer
- Skills listed (inferred): Python, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### Role Summary

KDCI Outsourcing is looking for a LangChain Developer to build LLM-powered applications using the LangChain framework. You'll design chains, agents, and retrieval pipelines that power intelligent product features.

### Key Responsibilities

Design and build applications using the LangChain (or LangGraph) framework

Develop chains, agents, and tool integrations for LLM-powered workflows

Build retrieval-augmented generation (RAG) pipelines with vector databases

Test and optimize prompt chains for accuracy and performance

Integrate LangChain applications with back-end systems and APIs

Stay current with updates to the LangChain ecosystem

### Requirements

### Requirements

2+ years of experience in software development, with hands-on LangChain experience

Strong Python skills

Experience with LLM APIs (OpenAI, Anthropic, or open-source models)

Familiarity with vector databases (Pinecone, Weaviate, Chroma, etc.)

Understanding of prompt engineering and RAG architecture

### Nice-to-Haves

Experience with LangGraph or multi-agent orchestration

Familiarity with LlamaIndex or similar frameworks

Background in NLP or conversational AI

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Akdci1%3A2682716 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.