Job description

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
Integration of LLM components into live chatbot and AI products
Evaluation and continuous improvement of retrieval quality
Technical documentation and knowledge sharing across the team

Good to Have

Experience with agentic frameworks (AutoGen, CrewAI, or custom agents)
Exposure to graph-based RAG or knowledge graph integration
Open-source contributions in the AI/ML space

What We Offer

Work directly with top leadership in a high-impact AI engineering role.
Be part of an innovative and fast-growing AI startup shaping the future of LLM applications.
Access to frontier models, compute resources, and cutting-edge tooling.
Perks & Benefits: gym membership, workation policy, and company-sponsored lunch.
A collaborative, ambitious, and fast-paced engineering environment.

If you're passionate about building reliable, production-grade LLM systems and want to work at the frontier of applied AI, we'd love to hear from you!

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