Algo / AI Engineer

Description About the company Flexor is a leading AI infrastructure company building the context layer that makes enterprise AI actually work. Backed by strategic investors, our technology transforms unstructured data, including documents, emails, contracts, call transcripts, and more, into clean, governed, AI-ready context using cutting-edge LLMs and VLMs.

We help enterprises move from generic AI to business-aware applications and AI agents they can actually trust. Our team operates at the intersection of data, applied AI, and enterprise software, and we're building fast.

About the role

As an Algo/AI Engineer at Flexor, you'll design and implement the core algorithms that power our AI Context Engine. You'll work on some of the hardest problems in enterprise AI: extracting structured, trustworthy context from massive volumes of noisy, heterogeneous unstructured data using LLMs, VLMs, and custom ML pipelines.

This is a high-impact role at the technical core of the product.

Responsibilities

Design and develop scalable algorithms for unstructured data extraction, transformation, and context enrichment Build and optimize LLM and VLM-based pipelines for production enterprise use cases Collaborate with data engineering and product teams to integrate AI solutions end-to-end Conduct rigorous evaluation and validation of model outputs for accuracy, consistency, and reliability Contribute to research and development of new approaches at the frontier of applied AI

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field 4+ years of experience in algorithm development or applied ML/AI engineering Hands-on experience with LLMs, VLMs, or other foundation models in production settings Proficiency in Python; experience with frameworks such as Py. Torch or equivalent Strong understanding of machine learning fundamentals and data structures Experience working with unstructured data (text, documents, audio, or multimodal inputs) is a strong plus Excellent analytical thinking and a track record of shipping robust AI systems