Senior AI Engineer

About VerbaFlo.ai VerbaFlo.ai is a fast-growing AI SaaS startup revolutionizing how businesses leverage AI-powered solutions. As part of our dynamic team, you’ll collaborate with visionary leaders and top-tier talent to shape the future of intelligent, scalable software products. We're on a mission to make cutting-edge AI accessible, impactful, and intuitive. Role Overview We are looking for a Senior AI Engineer with deep specialisation in Retrieval-Augmented Generation (RAG) and embedding systems. The ideal candidate has a proven track record of building end-to-end AI solutions — from ideation through to production deployment — and thrives at the intersection of language models, vector search, and applied NLP. This is a hands-on, high-ownership role. You will design and ship RAG pipelines, own embedding infrastructure, integrate AI capabilities into chatbot products, and work with diverse data sources to build systems that actually work in production. Responsibilities RAG pipeline ownership — Ideate, architect, build, and deploy end-to-end RAG systems from scratch through to production Embedding systems — Select, evaluate, and fine-tune embedding models; manage vector stores and optimise retrieval quality Advanced chunking — Implement late chunking and other segmentation strategies to maximise context fidelity and retrieval precision Multi-source data integration — Connect and ingest from diverse sources including SQL/NoSQL databases, PDFs, web content, Confluence, SharePoint, and real-time APIs Chatbot integration — Embed RAG and LLM components into conversational AI products using LangChain, LlamaIndex, or custom orchestration layers Evaluation & quality — Own retrieval evaluation frameworks (RAGAS, triad evals) and iterate on pipelines based on precision, recall, and relevance metrics Deployment & observability — Deploy and monitor LLM services on cloud infrastructure with robust logging, alerting, and MLOps practices Collaboration — Partner with product and engineering teams to deliver low-latency, reliable AI experiences at scale Requirements 5+ years of total engineering experience, with 3+ years in LLM or NLP engineering Hands-on experience designing and deploying RAG systems end-to-end Deep familiarity with embedding models (OpenAI Ada, Cohere, BGE, E5) and vector databases (Pinecone, Weaviate, Chroma, pgvector) Strong command of Python and LLM orchestration frameworks (LangChain, LlamaIndex, Haystack) Experience working with multiple data source types — structured, unstructured, and real-time Practical knowledge of late chunking and other advanced retrieval strategies Familiarity with cloud deployment (AWS / GCP / Azure) and containerisation (Docker, Kubernetes) Strong problem-solving instincts and a bias for building things that work in production What You'll Own / Deliver End-to-end RAG pipelines — from data source to retrieval to response Embedding infrastructure and vector store management …