LLM Integration / LangChain Engineer

Zoho; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Fyerx in the United States seeks an experienced LLM Integration / LangChain Engineer to design, develop, and implement orchestration layers connecting enterprise data assets with cutting-edge generative AI models.

You will build production-grade Retrieval-Augmented Generation (RAG) pipelines, program multi-agent reasoning loops using LangChain, LangGraph, or LlamaIndex, and establish secure system middleware to safely deploy AI capabilities at scale.

Qualifications

• 4 to 8 years of enterprise backend/web engineering or data pipelines experience.
• 2+ dedicated years writing production-level code around LLM infrastructures.
• Strong mastery of Python or TypeScript, vector representations, and prompt engineering.

Responsibilities

• Design and construct advanced LLM applications and orchestrations using LangChain, LangGraph, LlamaIndex or AutoGen.
• Build production-grade RAG architectures with dynamic context chunking and reranking.
• Develop multi-agent reasoning chains, tool calls, memory caching, and guardrail validations.
• Expose and consume high-throughput API endpoints linking OpenAI/Anthropic/HuggingFace models with internal data sources.

Skills

Python/TypeScript

LLM fundamentals

Async Web Apps

SQL

Education

ML/Cloud cert (AWS/GCP/Azure)

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

GPTCache

Job description

• Relevant Experience Required: 2+ years of dedicated hands-on experience building, integration, and deploying applications powered by Large Language Models (LLMs)
  

We are seeking an experienced LLM Integration / LangChain Engineer to design, develop, and implement the orchestration layers connecting our enterprise data assets with cutting-edge generative AI models. The ideal candidate will build production-grade Retrieval-Augmented Generation (RAG) pipelines, program multi-agent reasoning loops using LangChain, LangGraph, or LlamaIndex , and establish secure system middleware to safely deploy AI capabilities at scale.

Key Responsibilities

• Design and construct advanced LLM applications and orchestrations using specialized frameworks like LangChain , LangGraph , LlamaIndex , or AutoGen .
  
• Build production-grade Retrieval-Augmented Generation (RAG) architectures, configuring dynamic context chunking, document parsing, semantic metadata tagging, and reranking pipelines.
  
• Develop complex multi-agent reasoning chains and workflows, implementing custom tool calling structures, memory caching architectures, and guardrail validations.
  
• Expose and consume programmatic endpoints , constructing high-throughput API integrations connecting foundational LLMs (e.g., OpenAI , Anthropic , open-source models via Hugging Face/Ollama ) with internal corporate databases and CRMs.
  
• Apply rigorous AI evaluation and prompt tracking structures , utilizing observability platforms (e.g., LangSmith , Arize Phoenix ) to monitor token usage bounds, model latency, and prompt generation drift.
  
• Implement secure middleware execution barriers, configuring text sanitization, PII data-masking pipelines, prompt injection defensive rings, and toxicity filtering parameters.
  
• Optimize model inference costs and context window budgets , designing custom semantic caching frameworks (e.g., GPTCache ) to intercept recurring operational queries.
  

Requirements

• 4 to 8 years of core enterprise backend web engineering or data pipelines experience, with 2+ dedicated years actively writing production-level application code wrapped directly around LLM infrastructures.
  
• Strong technical mastery of Python or TypeScript, vector representations, prompt engineering grounding mechanics, asynchronous web frameworks (FastAPI), and SQL.
  
• Deep structural understanding of transformer model designs, text embedding properties, agentic tool execution cycles, and API orchestration limits.
  
• Mandatory certification: Professional-level machine learning or cloud developer certification from a major cloud vendor (AWS/GCP/Azure).
  

Preferred Qualifications

• Familiarity with deploying AI applications within container systems (Docker, Kubernetes) integrated into modern DevSecOps CI/CD delivery loops.