Epiq
Senior Consultant, Product Engineer
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At Epiq, your work contributes to complex, global legal outcomes. You’ll join a values-driven community where integrity guides decisions, relentless service sets the bar, and we thrive on big challenges together. We invest in your growth with enterprise-wide learning and mobility. We celebrate who you are, and we respect life beyond work with flexibility that’s recognized externally. Enabled by modern platforms and AI, you’ll do the most meaningful work of your career and see your impact at scale.
Job Description:
Job Summary
We are looking for a Product Engineer to join our AI Search and Retrieval Context Engineering as we build the next generation of legal AI experiences for law firms.
This is not a traditional front-end developer role where requirements are handed over and built from a specification. It is a hands-on product engineering role for someone who can understand the problem, shape the user experience, think through the system design and then build high-quality software.
The role is primarily focused on React, TypeScript and modern user experience development, but the expectation is broader than UI delivery. You will work close to lawyers, knowledge teams, innovation teams and technical stakeholders to understand how AI search, retrieval and agents should fit into real legal workflows.
We are not building another disconnected AI chatbot. We are building trusted AI experiences that sit on top of the firm’s own documents, knowledge, matters, metadata and workflows.
As a Product Engineer, you will help turn complex AI retrieval capability into simple, useful and trusted experiences for lawyers.
Why This Role Matters
AI is changing software engineering. The value of an engineer is no longer only in writing code quickly. AI can now help generate code, tests and components at speed. That makes the human part of engineering more important, not less.
The best engineers are now the ones who understand the system, the user, the constraints and the business problem.
For this team, that means understanding why a lawyer is searching, what they need to trust an answer, what happens if the wrong document is surfaced, how permissions work, how citations should be displayed, and how the experience fits into their daily workflow.
A button is not just a button. A search result is not just a card. An AI answer is not just text on a page.
In legal AI, every interaction has to support trust, explainability, security and adoption.
That is why we need Product Engineers: people who are fluent in engineering, fluent in product thinking, and willing to understand the domain deeply enough to build the right thing.
About The Team
The Search & AI team helps law firms build secure, firm-specific AI capabilities on top of their existing document, knowledge and matter infrastructure.
We work with firms that want to move beyond standalone AI tools and create a trusted AI layer inside their own environment. Our solutions typically connect to systems such as iManage, SharePoint, NetDocuments, Microsoft 365, Azure AI Search, Azure OpenAI and internal knowledge platforms.
The team already has experience delivering AI search and workflow capabilities for a Magic Circle law firm and a global multinational law firm, including natural language search, agents and Azure-based retrieval solutions built in partnership with Microsoft.
As the market shifts from AI adoption to AI ownership, our team is becoming more like a Forward Deployment Engineering function: technical people who work close to strategic clients, understand their workflows, and build real solutions that move from prototype to production.
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The Role
You will design and build the product experience layer for legal AI.
That includes search interfaces, AI answer experiences, document previews, citation views, agent workflows, feedback loops, dashboards, admin screens and embedded components for tools lawyers already use.
You will mainly work in React and TypeScript, building polished, enterprise-grade applications that are fast, accessible, secure and suitable for professional legal users.
You will work with AI engineers, retrieval architects, Azure engineers, legal knowledge experts and client stakeholders to turn complex technical capabilities into clear, usable products.
What You Will Build
- You may build a natural language search experience that allows lawyers to ask questions across a firm’s knowledge bank and receive grounded answers with citations.
- You may create a source preview panel that lets a lawyer inspect the exact document behind an AI answer before relying on it.
- You may design an agent workflow where a lawyer can run a research task, review retrieved documents, refine the question, compare outputs and export the result.
- You may build dashboards that show search quality, adoption, answer confidence, feedback trends and retrieval performance.
- You may create embedded components for SharePoint, Teams, Word, Outlook or a firm intranet, so AI appears inside the workflow rather than as another destination.
The common thread is that you will be building the experience layer that makes AI usable, explainable and trusted.
Key Responsibilities
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Build high-quality React and TypeScript applications
You will build modern front-end applications using React, TypeScript and strong component design. You will care about performance, accessibility, responsiveness, error handling, loading states, empty states, maintainability and the overall quality of the user experience. You will work with APIs, authentication, permissions, search services, AI responses, document metadata and workflow actions.
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Shape the product, not just the code
You will help define how legal AI experiences should work. This means challenging unclear requirements, simplifying complex workflows, proposing better interaction patterns and making sure the team is building something users will actually adopt. You will not just wait for a perfect specification. You will help create it.
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Understand the lawyer workflow
You will work close to legal users and client teams to understand how lawyers search for knowledge, review documents, validate answers and move through their work. The goal is to build AI into workflows lawyers already understand, rather than sending them to another disconnected tool.
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Turn AI retrieval into trusted UX
You will design interfaces that make retrieval transparent. This includes citations, source previews, metadata, snippets, filters, document cards, confidence signals, feedback mechanisms and audit-friendly interaction patterns. In legal AI, the user needs to understand where an answer came from and why it should be trusted.
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Work across the system
Although this is mainly a React and TypeScript role, you will need to understand how your UI fits into the wider system. That may include Azure AI Search, Azure OpenAI, retrieval APIs, MCP-style tool access, document processing pipelines, security trimming, Microsoft Graph, SharePoint, iManage or other enterprise systems. You do not need to own every layer, but you should understand how the layers connect.
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Use AI as part of your engineering workflow
We expect Product Engineers to use AI effectively in their own work. That may include using AI to generate code drafts, test ideas, explore components, accelerate refactoring or support documentation. But the judgement remains yours. AI can help write code. It cannot own the product intent, system consequences or user trust.
Required Skills
- You should have strong hands-on experience with React and TypeScript.
- You should be comfortable building component-based applications, working with APIs, handling complex state and delivering polished enterprise user interfaces. Experience with Next.js would be useful, but is not essential.
- You should understand modern front-end development practices, including authentication flows, role-based permissions, asynchronous data, loading states, error states, accessibility, testing, source control and CI/CD.
- Experience with some of the following would be valuable: React Query, Zustand, Redux, Tailwind, Fluent UI, Material UI, Vite, Next.js, Storybook, Playwright, Jest, Azure Static Web Apps, Azure App Service or Microsoft Graph.
- You do not need to be a deep AI engineer, but you should be interested in AI product design and willing to learn how retrieval, citations, agents and RAG systems work.
Product Engineering Mindset
The strongest candidates will think beyond tickets.
- You should be someone who wants to understand why something is being built, not just how to build it.
- You should be comfortable working in ambiguity, forming a point of view, testing ideas quickly and improving based on feedback.
- You should care about systems. You should want to understand how the UI, API, retrieval layer, permissions model and user workflow fit together.
- You should be able to see where a small UI decision could create confusion, risk or unnecessary friction.
- You should also care about product quality. Legal users are time-poor and sceptical of poor AI experiences. The interface needs to be clear, reliable and professional.
This is a role for someone who wants to use engineering judgement, product instinct and domain understanding together.
AI and Search Awareness
You will work around AI search and retrieval systems, so you should be interested in concepts such as retrieval-augmented generation, semantic search, vector search, hybrid search, document chunking, citations, source grounding, prompt workflows and agentic interfaces.
You do not need to build all of this from scratch, but you do need to understand enough to design good user experiences around it.
For example, when an AI answer is shown, the UI should help the lawyer answer practical questions: Where did this answer come from? Can I open the source? Is this the latest version? Which jurisdiction does this relate to? Who authored it? Can I trust this document? What should I do next?
That is product engineering in legal AI.
Desirable Experience
Experience in any of the following would be useful: legal technology, enterprise search, document management systems, knowledge management, Microsoft 365, SharePoint, iManage, NetDocuments, Azure AI Search, Azure OpenAI, Microsoft Graph, Power Platform, SaaS products, internal tools, workflow platforms or professional services.
Experience building search interfaces, AI interfaces, dashboards, document review tools, knowledge portals or workflow applications would be particularly
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