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 a Data Engineer at Flexor, you'll design and run the core systems behind our AI Context Engine, building scalable ingestion, transformation, and storage pipelines, developing APIs and services, and bringing LLMs and ML models into production. You'll work at the intersection of data engineering, infrastructure, and applied AI, on systems that need to be fast, reliable, and built for scale.

Responsibilities



• Build and maintain scalable data ingestion, transformation, and storage pipelines
• Develop efficient ETL workflows and data APIs that power the Flexor AI Context Engine
• Integrate LLMs and machine learning models into production systems
• Optimize infrastructure for performance, reliability, and scale
• Collaborate with the AI/algorithms team to productionize ML components
• Build infrastructure for training, fine-tuning, and serving LLMs at scale

• 6+ years of Python and Data Engineering experience
• Strong SQL skills and proficiency with cloud platforms (GCP, AWS)
• Hands-on experience with distributed systems, APIs, and containerized environments (Kubernetes)
• Experience building and optimizing ETL workflows and data pipelines

Strong plus:



• Experience with unstructured data sources
• Hands-on LLM or ML integration in production environments
• Familiarity with the latest developments in AI, including LLMs and VLMs

You'll thrive here if you:



• Love solving deep engineering challenges in distributed systems and data infrastructure
• Can design and optimize data pipelines, APIs, and ML services end-to-end
• Enjoy working at the intersection of data engineering and applied AI
• Want to work on systems where LLMs and VLMs are first-class components, not an afterthought