LLM Integration / LangChain Engineer

This is a remote position.

LLM Integration / LangChain Engineer

Job Details

• Employment Type: Contract
  
• Work Mode: Remote
  
• Location: Offshore
  
• Total Experience Required: 4 to 8 years
  
• Relevant Experience Required: 2+ years of dedicated hands-on experience building, integration, and deploying applications powered by Large Language Models (LLMs)
  
• Mandatory Certification: Developer certification from a major AI or Cloud platform (e.g., Google Cloud Certified Professional ML Engineer, AWS Certified Machine Learning - Specialty, or verifiable framework specialization credentials)
  

Job Summary
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

• Prior experience fine-tuning open-source LLMs (e.g., Llama, Mistral) via quantization techniques like QLoRA or LoRA frameworks.
  
• Familiarity with deploying AI applications within container systems (Docker, Kubernetes) integrated into modern DevSecOps CI/CD delivery loops.