AI Engineer - NLP

AI Engineer - NLP Company Description AJARI TECHNOLOGIES is a technology company focused on an AI-powered learning and workforce development platform. Our solutions are designed for governments, institutions, schools, universities, and large organizations to personalize education, upskill, and transform learning outcomes at a national level. We work to scale education to meet the diverse needs of our clients, ensuring impactful results and continuous improvement.

About the Role This is a full-time on-site role for an AI Engineer (NLP). We are looking for an AI Engineer (NLP) to build and develop NLP and Large Language Model systems from research to production, with a focus on agentic AI, RAG, and system reliability at scale.

Responsibilities: Agentic AI & Orchestration: Design and develop agentic AI systems with multi-step reasoning, tool calling, and human-in-the-loop workflows. Implement stateful workflows using LangGraph or LlamaIndex. Integrate various tools and external APIs into LLM workflows. RAG (Retrieval-Augmented Generation): Build end-to-end RAG pipeline: document chunking, embedding generation, retrieval strategies, and reranking. Optimize retrieval quality with hybrid search and relevance metrics evaluation. Manage vector database for semantic search at scale Natural Language Processing: Apply LLMs for NLP tasks such as summarization, information extraction, text classification, sentiment analysis, and document understanding. Build NLP solutions for specific use-cases such as question answering, semantic search, and content generation.

Must Have: Experience building agentic AI-based systems using LangGraph or LlamaIndex. Understand function calling patterns and integrating external APIs into LLM workflows. Have expertise in Retrieval-Augmented Generation (RAG), including document chunking, embeddings, retrieval, and reranking. Proficient in using vector databases for semantic search. Practical experience applying LLMs for summarization, information extraction, text classification, and document understanding.

Nice to Have: Prompt engineering skills and context window optimization. Experience with LLM evaluation frameworks and quality metrics. Ability to design production-grade APIs. Experience using asynchronous Python for scalable LLM applications. Experience with multimodal (text and vision). Experience implementing WebSocket and Server-Sent Events for streaming responses.

Tech Stack: Python PyTorch / TensorFlow Hugging Face LangGraph LlamaIndex FastAPI WebSocket / SSE Qdrant vLLM Git Redis PostgreSQL / MongoDB