LLM Integration / LangChain Engineer
FyerX
- Location
- Remote
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
FyerX is seeking an experienced LLM Integration Engineer to design and deploy production-grade AI applications, RAG pipelines, and multi-agent workflows. The role focuses on connecting enterprise data with foundation models while ensuring security, observability, and cost-efficiency.
Skills
Python
TypeScript
LangChain
LangGraph
LlamaIndex
Retrieval-Augmented Generation
Multi-Agent Systems
Prompt Engineering
Vector Embeddings
FastAPI
SQL
API Integration
AI Evaluation
PII Data Masking
Docker
Kubernetes
Perks
- Remote Work
Full job details
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.