Senior Technical Product Manager, Doc Platform & AI Infrastructure

Job description

Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency. Powered by **LOIS**, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest-growing technology companies in the country. **Role Overview** We are seeking an infrastructure-focused Senior Technical Product Manager to lead the development of the document processing platform that powers both our core legal practice management application and our growing suite of AI-driven features and capabilities. You will ensure delivery of a coherent, performant, and scalable data management system covering document storage, content extraction, indexing, chunking, embedding, and multi-modal search. This role will work in close partnership with a dedicated engineering team to solidify and extend the platform, collaborating with peer PMs and client engineering teams to drive adoption of reliable, accurate, high-performance solutions that scale our next-generation LOIS AI experiences. ### What You'll Do: - **Platform & Search Ownership:** Define the platform vision and develop a comprehensive understanding of the architecture, capabilities, and limitations across all owning and consuming teams, translated into a clear strategy and prioritized roadmap that balances platform maturity with delivery of new functionality. - **Technical Requirements:** Partner with platform engineers and technical leads to define requirements, constraints, and success criteria for platform investments incorporating infrastructure capabilities and customer needs into clear, actionable specifications that give engineering teams the context and clarity to make sound decisions on topics such as OCR pipelines, chunking strategies, indexing technologies, embedding and vector storage, and related infrastructure. - **Document Infrastructure:** Partner closely with backend engineering, infrastructure teams, and data science to drive efficiency, improve consistency, simplify processing, and align document management functionality with the broader platform. - **Feature Enablement:** Own the adoption and migration strategy for platform functionality, working through influence, demonstrated value, and consensus-building to drive teams toward shared solutions. Collaborate with peer PMs responsible for both AI-powered and traditional user-facing features to ensure platform capabilities are well-positioned to support new product requirements. - **Data-Driven Optimization & Metrics:** Monitor, analyze, and optimize performance,latency, cost-to-serve, and core engineering metrics. In collaboration with machine learning engineers, track AI platform outcomes, including data extraction accuracy, ingestion throughput, indexing latency, and search precision/recall. - **Evangelism & Mentorship:** Proactively build awareness of document platform capabilities across the organization. Mentor Associate and mid-level Product Managers in technical product processes, data-driven decision making, and infrastructure leadership. ### Who You Are: - **Experience:** 5+ years of software product management experience in SaaS, with a proven track record of responsibility for technically complex products or platforms. Candidates with fewer years but strong relevant domain experience (e.g., document management systems, search and indexing infrastructure, RAG or LLM pipelines, or legal technology) will be given serious consideration. - **Technical Depth**: Demonstrated knowledge of software development methodologies and sufficient technical fluency to engage substantively with engineers on topics like document processing pipelines, full-text and vector search, or content extraction, with particular value placed on shipping data-heavy or AI/ML-adjacent features responsibly and securely at scale. - **Platform Leadership:** Comfort operating in a complex, multi-team environment and experience managing platform or shared-service products where adoption is driven by value rather than mandate, including the ability to build consensus across teams, navigate competing priorities, and manage complex cross-team migrations. - **Analytical Mindset:** Strong analytical skills and a data-driven mindset, experience defining and tracking core product outcomes (adoption, accuracy, user confidence) along with the ability to identify where platform investment creates compounding leverage versus where it introduces unnecessary constraints or overhead - **Execution & Agility:** Strong experience working within Agile/Lean delivery frameworks, with an emphasis on technical experimentation, iteration, and continuous system enhancement. - **Communication Skills:** Excellent communication and interpersonal skills with the ability to translate highly complex technical tradeoffs and AI concepts into clear narratives for both engineering teams and non-technical stakeholders. ### Nice-to-Have - Prior product management experience in Legal Tech, Legal AI, or adjacent compliance-heavy industries, particularly in areas touching document discovery, production, practice management, or other document-dense applications - Direct familiarity or hands-on experience with one or more of the following: scaled distributed document processing or management systems; Opensearch, Elasticsearch or comparable indexing and search technologies; OCR and content extraction pipelines; vector databases and embedding strategies; generative AI context engineering; hybrid search architectures;