This is a remote position.

LLM Integration / Lang Chain 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 / Lang Chain 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 Lang Chain, Lang Graph, or Llama Index, 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 Lang Chain, Lang Graph, Llama Index, or Auto Gen.

- 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., Lang Smith, 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 Type Script, 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 up-to-date Dev SecOps CI/CD delivery loops.

📌 LLM Integration / LangChain Engineer (India)
🏢 FyerX
📍 India