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Full Stack Software Engineer – AI Applications

Alexandria Technology

Salary not specified
Sep 10, 2025
New York, NY, US
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Alexandria Technology is building next-generation AI-powered products that transform the way companies analyze data. Our mission is to bring cutting-edge AI into business applications that are fast, scalable, and impactful.

Requirements

  • Proficiency in both frontend (React, TypeScript) and backend (Python) development
  • Experience working with distributed systems and large-scale data pipelines
  • Hands-on experience with Apache Cassandra for scalable data storage
  • Working knowledge of Apache Arrow for high-performance data processing
  • Experience with LangChain or other LLM frameworks
  • Knowledge of retrieval-augmented generation (RAG) pipelines
  • Experience working with vector databases and hybrid search systems

Responsibilities

  • Build and maintain scalable web applications powered by AI backends
  • Design and implement APIs that integrate with large language models (LLMs), vision models, and custom ML pipelines
  • Work with Cassandra And Solr to house and serve big data in a fast, scalable and robust manner
  • Utilize Apache Arrow to process and transfer columnar data efficiently, especially for analytics and model-serving pipelines with massive datasets (1+ billion rows)
  • Collaborate with ML engineers to productionize models and optimize inference performance
  • Develop responsive UI components that help users interact with AI intelligently and transparently
  • Feed your application runtime data into timerseries databases (ie. Prometheus) coupled with monitoring and alerting logic (ie. Grafana) to assist with resolution of issues and outages

Other

  • 3–6+ years of professional experience as a full stack engineer
  • Familiarity with real-time or near-real-time data applications
  • Strong communication skills and a collaborative mindset
  • Passion for AI and eagerness to stay ahead of emerging trends
  • Interest in prompt engineering, model fine-tuning, or AI safety best practices