Role Description:

This professional specializes in building and integrating Large Language Model (LLM)-based agents into existing workflows, particularly for content creation, research, SEO enhancement, and automated auditing. Their focus is on leveraging frameworks like LangChain (or similar LLM orchestration libraries) and local LLM deployments to create pipelines that:

• Crawl the web or internal data sources for relevant information.
• Use LLM-based “agents” to fact-check, summarize, and refine this information.
• Implement “humanizers,” i.e., processes or prompt strategies that give generated text a more authentic and natural human tone.
• Integrate with SEO tools or content management systems to enhance discoverability and ranking of produced content.

Required Experience and Skills:

Technical Foundations:

• Proficiency in Python: Strong coding skills to implement and customize LangChain pipelines, integrate with APIs, and handle data processing.
• LLM Tools & Frameworks: Hands-on experience with frameworks like LangChain, Haystack, or LlamaIndex; comfortable running open-source LLMs locally (e.g., LLaMA, Falcon, MPT).
• Prompt Engineering & Prompt Chaining: Ability to craft, refine, and systematically test prompts to achieve desired behavior from LLMs.
• Agent-Based Frameworks: Understanding how to create and manage autonomous agents that can reason, retrieve, and act on tasks without constant human supervision.

Data & DevOps Skills:

• Data Integration: Experience integrating multiple data sources (APIs, websites, databases) into LLM workflows.
• Database & Vector Storage: Familiarity with vector databases or embeddings stores (e.g., Pinecone, Chroma, Weaviate) for semantic search and context retrieval.
• Deployment & Scaling: Comfortable with containerization (Docker), possibly orchestration (Kubernetes), and knowledge of GPU provisioning for running local LLMs.
• Version Control & CI/CD: Experience maintaining reliable code repositories, using Git, and implementing testing frameworks for continuous improvement.

Content & SEO Knowledge:

• SEO Principles: Understanding how content is ranked and how to ensure LLM outputs align with SEO best practices (keyword usage, metadata formatting, semantic structure).
• Content Quality & Fact-Checking: Skilled at designing flows to cross-reference facts, filter out misinformation, and maintain content accuracy.
• NLP & NLU Knowledge: Basic understanding of natural language understanding, entity recognition, summarization, and sentiment analysis to produce high-quality, humanized content.