Jobs at Onit

Supports legal AI product development through legal workflow research, risk assessments, compliance documentation, prompt engineering, jurisdictional regulatory research, and independent validation. The role evaluates AI outputs for legal, ethical, privacy, trust, and safety requirements; maintains risk registries and audit logs; prepares model factsheets and privacy statements; and collaborates with product and engineering teams to deliver trustworthy, compliant AI solutions.

Build and ship production agentic AI features using LLM APIs, tool-calling, multi-agent workflows, RAG, and context engineering. Establish evaluation frameworks, regression gates, monitoring, observability, and safety guardrails. Develop scalable backend services and integrate AI into cloud and event-driven architectures. Partner with product and domain teams to prototype solutions, document failure modes, and improve quality, cost, latency, reliability, and security through iterative experimentation.

Develop and execute UI, API, load, and performance tests using Playwright or Selenium with Artillery. Write automated tests in JavaScript or Java, validate data with SQL, integrate testing into CI/CD pipelines, analyze failures, perform root cause analysis, and collaborate with development and product teams. The role also involves testing AI-driven features and using AI coding tools to accelerate test development and debugging.

Builds substantial full-stack features for an enterprise legal operations platform using Ruby on Rails and modern JavaScript frameworks. Responsibilities include developing APIs and data models, creating accurate data-dense interfaces and visualizations, implementing filtering and asynchronous workflows, handling loading and error states, ensuring accessibility and cross-browser compatibility, instrumenting usage, and writing comprehensive automated tests. The role partners with design, product, and architecture teams while reviewing AI-generated code and working within a large existing codebase.

Build and operate Databricks data pipelines for Onit’s customer-facing benchmarking platform. Responsibilities include integrating and modeling multi-source data, resolving duplicate records, implementing data quality checks, orchestrating reliable workflows, optimizing Spark performance and costs, and applying governance, privacy, and multi-tenant isolation. The role collaborates with architects, data scientists, product, and engineering teams to productionize analytics and expose trustworthy curated data.

Build and maintain substantial full-stack features for an enterprise legal operations platform. Develop Ruby on Rails APIs, modern JavaScript interfaces, data visualizations, filtering and comparison controls, asynchronous workflows, accessibility features, instrumentation, and automated tests. Collaborate with design, product, and architecture teams while ensuring performance, accuracy, robust edge-case handling, and compatibility with large existing codebases. Use AI coding tools responsibly to accelerate development, debugging, and testing.

Provides front-line technical and product support for Onit’s SaaS legal operations platform through phone, email, and online channels. Responsibilities include troubleshooting, educating users, managing software subscriptions, resolving customer escalations, supporting implementations, contributing product feedback, and completing projects. The role also assists and coaches colleagues while working independently in a fast-paced technology environment.

Designs and maintains UI, API, and integration test automation for an AI-driven legal operations platform. Develops Playwright and JavaScript frameworks, integrates testing with Allure and Jenkins CI/CD pipelines, analyzes failures, performs root cause analysis, establishes test data strategies and quality metrics, reviews automation code, and collaborates with engineering and product teams. The role also evaluates AI outputs, builds adversarial datasets, and supports agent and MCP testing.

Supports sales through tailored product demonstrations, RFP/RFI responses, solution configuration, proof-of-concepts, and technical customer engagement. Collaborates with sales, product, engineering, legal, and marketing teams; attends trade shows and customer events; communicates technical and business value to diverse stakeholders; gathers market feedback; and contributes to webinars, case studies, and technical content.

Own product strategy and multi-year roadmaps for major areas of an AI-native enterprise legal operations platform. Lead discovery, requirements, prioritization, delivery, and iteration while aligning Engineering, Design, GTM, Sales, Marketing, and Customer Success. Engage customers, define outcome metrics, guide AI/ML and platform decisions, support launches, influence executives, and mentor product managers.

Design and build production-grade data pipelines, services, APIs, and data models powering analytics and AI features. Optimize performance and scalability, implement data validation, monitoring, security, and multi-tenancy, and lead troubleshooting. Establish technical approaches, participate in reviews and CI/CD, collaborate across product and data teams, and mentor engineers.

Develop and launch modules, APIs, and product features for Onit’s legal technology platform. Work across backend and frontend development using Ruby on Rails, React, JavaScript, and PostgreSQL; collaborate with designers and project managers; estimate tasks; automate testing; and contribute within an Agile, AWS-based development environment. The role emphasizes backend expertise with growth toward full-stack development and mentoring junior developers and QA resources.

Build and maintain data pipelines, backend services, and APIs supporting analytics and AI features. Develop SQL queries, data models, validation checks, automated tests, and production-grade code. Troubleshoot data and application issues, participate in Agile ceremonies and code reviews, document solutions, and collaborate with senior engineers, architects, product managers, data scientists, and QA.

Own the statistical methodology for a customer-facing benchmarking product. Build comparability models, establish defensibility thresholds, improve data quality, resolve entities, assess external data, and define privacy-conscious governance for anonymized cross-customer analytics. Partner with Product, Engineering, and Data Architecture while communicating uncertainty clearly to nontechnical stakeholders.

Own the architecture of a multi-tenant, data-intensive analytics capability within an enterprise SaaS platform. Select the analytical data platform, design canonical data models, define API contracts, establish performance and availability requirements, and enforce tenant isolation. The role includes hands-on query and performance engineering, architecture decision records, design and code reviews, and translating technical constraints across Product, Design, and Engineering.

Provides frontline technical support for Onit’s SaaS legal operations products through phone, email, and online channels. Responsibilities include troubleshooting, educating users, maintaining product expertise, sharing product feedback, supporting implementations, and owning projects through completion. Requires technical support or software implementation experience, programming fundamentals, strong communication, documentation proficiency, and the ability to manage multiple priorities.

Own and evolve the enterprise modern endpoint environment across Windows and macOS. Design and administer Intune, Windows Autopilot, Entra ID, Conditional Access, application deployment, patching, compliance, endpoint security, and device lifecycle management. Build PowerShell and Graph automations, resolve complex L3 issues, manage endpoint projects through rollout and handoff, strengthen macOS capabilities, document processes, and mentor IT staff.

Owns and advances Microsoft Purview data protection and AI governance across Microsoft 365 and endpoints. Responsibilities include designing and tuning DLP policies, sensitivity labels, retention and eDiscovery controls; assessing SharePoint, OneDrive and Teams exposure; supporting Copilot and generative AI readiness; governing Shadow AI; automating operations with PowerShell and Microsoft Graph; troubleshooting complex L3 issues; and leading data-security projects, documentation and knowledge transfer.

Manage a portfolio of SMB customers by driving adoption, engagement, retention, and growth. Monitor customer health and usage trends, identify risks and opportunities, and provide proactive guidance. Build relationships with Legal Ops, Finance, and IT stakeholders while serving as a trusted advisor. Lead scalable engagement programs, resolve customer challenges, and collaborate with Implementation, Support, Marketing, Product, and Account Management teams.

Design and ship production-grade agentic AI systems, including tool-using agents, multi-agent workflows, RAG, context engineering, evaluation frameworks, observability, safety controls, and backend integrations. Partner with product and domain experts to turn ambiguous problems into reliable AI experiences. Own experimentation, regression testing, cost and latency monitoring, failure handling, governance, and safe deployment of AI capabilities.