Senior/Lead Fullstack Developer

A World Changing Mission Polysentry’s leading data analytics and intelligence solutions empower teams to swiftly convert vast amounts of global data into actionable and contextual insights, enabling organizations to make better decisions. We engage in close collaboration with customers in high-stakes industries, including financial services, legal, and government. The vital insights we deliver play a pivotal role in safeguarding critical assets on a global scale. ‍ Our Company Polysentry is a technology company that provides industry leading data discovery, classification and analysis solutions. Since 2018, Polysentry has been building and deploying mission-ready AI-based platforms that meet rapidly evolving security needs. The company’s software solutions are built for corporate and government organizations. ‍ Our Team Our innovative team is building cutting-edge Retrieval-Augmented Generation (RAG) systems that significantly improve information retrieval accuracy. We're working with the latest embedding models, vector databases, and LLMs to develop solutions that maintain critical context during retrieval operations. ‍ The Role We're looking for a Senior Engineer to help us build and optimize our advanced RAG platform. Your work will enable customers to unlock new levels of performance from their knowledge bases across various domains. ‍ Responsibilities Implement and optimize contextual embedding and preprocessing pipelines Design efficient document chunking and context generation workflows Build reranking systems that further improve retrieval accuracy Develop evaluation frameworks to measure retrieval performance across different domains Create cost-effective solutions using prompt caching and other optimization techniques Experiment with different embedding models to identify optimal configurations ‍ Requirements Experience with embedding models (Gemini, Voyage, or similar) Strong understanding of vector databases and similarity search Experience with prompt engineering for context generation Familiarity with document chunking strategies and preprocessing Understanding of reranking systems and their implementation Background in measuring retrieval accuracy (recall metrics) Programming experience in Python or similar language Experience with large-scale data processing ‍ Nice to Have Background in NLP, information retrieval, or computational linguistics Experience with RAG system architecture and implementation Knowledge of LLM prompt caching and optimization techniques Familiarity with domain-specific retrieval challenges (code, scientific papers, etc.) Experience balancing performance improvements against latency and cost considerations Background in developing or working with major enterprise LLMs Understanding of AWS or similar cloud infrastructure for LLM applications ‍ ‍ THINK YOU ARE A GOOD FIT? SUBMIT YOUR RESUME FOR THE POSITION TODAY! CONTACT@POLYSENTRY.COM