Jobs at Discernis

Design and improve production AI workflows for legal document intelligence using agentic orchestration, prompt engineering, retrieval, and model post-training. Own LLM evaluation and benchmarking frameworks, structured outputs, self-hosted model serving, inference optimization, and customer deployments. Build reliable, efficient, explainable systems using Python and modern LLM tooling, with emphasis on quality, latency, cost, and reproducibility.

Build and improve a high-performance frontend for legal document review, including document viewing, search, annotations, citation navigation, and AI result verification. Partner with backend and AI engineers through REST APIs, support a scalable component system, and ensure accessibility, performance, consistency, and secure handling of sensitive browser data.

Own the full enterprise sales cycle for AI document intelligence solutions sold to law firms and corporate legal teams. Manage complex, multi-stakeholder deals involving legal, IT, security, procurement, and executive buyers. Build pipelines, conduct discovery, lead demos and proofs of value with sales engineering, navigate security and deployment reviews, maintain accurate forecasts, and provide market feedback to product and leadership.

Serve as the technical lead for enterprise sales, running demos, technical discovery, proofs of value, security reviews, and customer pilots. Manage deployments, onboarding, adoption, issue resolution, and expansion opportunities while translating customer feedback to engineering. Engage IT, security, and infrastructure leaders on architecture, deployment, data handling, and compliance for cloud, on-premises, and customer-controlled environments.

Own and improve infrastructure across cloud, customer-managed cloud, on-premises, and air-gapped environments. Package the platform as a reproducible Kubernetes bundle, operate application and GPU workloads, manage infrastructure as code and CI/CD releases, and build monitoring, logging, and alerting for restricted environments. Responsibilities also include supporting search and data infrastructure, networking, secrets, certificates, and security hardening.

Build and maintain backend services, APIs, and document-processing pipelines for PDFs, Office files, email containers, and OCR workflows. Improve database and background-task performance, reliability, testing, observability, authentication, authorization, and secure system design. Collaborate with search and AI engineers on large-scale indexing and retrieval using Python, Flask, PostgreSQL, Redis, Elasticsearch, Docker, and related technologies.