Senior Director, Software Engineering - Contract Intelligence

Job description

As the most trusted global leader in data-first contract lifecycle management (CLM) software, Agiloft helps organizations manage the end-to-end process of proposing, negotiating, signing, and leveraging contracts using our flexible Data-first Agreement Platform (DAP). With contract data as the foundation, customers quickly and collaboratively reach agreement and leverage contract visibility to thrive with competitive advantage. Employing powerful, pragmatic artificial intelligence as a legal force multiplier, and robust integration capabilities as a data liberator, organizations around the world trust Agiloft’s certified implementers to deliver connected, intelligent, and autonomous solutions across the entire contract lifecycle. Top analysts like Gartner, Forrester, and IDC agree, all showing Agiloft as a leader in the CLM space. Our no code platform is easily managed and administered by business users, which is why Agiloft is the contract you keep: nearly a full 100% of new customers are satisfied with their initial implementations, and some 97% of customers renew every year. Ours is a growing, vibrant, successful company that is at the forefront of a market that is becoming a must-have for all organizations. We believe that the way to build the strongest, most vibrant place to work is to bring in individuals from all walks of life, and to support them in bringing their authentic selves to their day, every day. Our working philosophy is that “EX = CX”: when employee experience is excellent, so is customer experience. We support multiple Employee Resource Groups (ERGs), and offer a working environment that supports healthy work/life balance, including floating holidays and a quarterly, no-questions-asked wellness day. ## Position Overview AI is changing CLM, and Agiloft is building what comes next. As Senior Director, Software Engineering - Contract Intelligence, you will lead the engineering teams behind Astra Agents and the Contract Intelligence Graph (CIG). These are the capabilities that turn contracts into intelligence customers act on every day. You will own engineering strategy, organization, and execution across this portfolio, together with the shared platform components and interfaces that serve these experiences. This is a rare chance to shape and build a converged platform that makes these products work as one and that future Agiloft experiences will build on. The shared platform scope includes common data models and database services, identity and access management (IAM), agent and AI Assistant capabilities, and a unified interface suite spanning APIs, user interfaces, and MCP servers. You will decide how each product consumes these capabilities while preserving the flexibility each experience needs. Reporting directly to the CTO and working closely with Product, Design, Applied AI & Knowledge Engineering, Core CLM Engineering, and Cloud Operations, you will turn ambitious product priorities into an integrated technical roadmap and dependable delivery that drives measurable customer value. You will lead managers, technical leaders, and distributed teams. This role is for a leader who stays close to the code, holds a high bar, and builds a culture where strong engineers do their best work. Success means shared capabilities are adopted across Astra and CLM, the architecture remains secure and scalable, releases are predictable, and the organization can deliver customer value without sacrificing quality, reliability, or long-term maintainability. If you want to shape how enterprises manage their contracts in the AI era, and build the team that makes it happen, we’d love to talk with you. ### Job Responsibilities ### Contract Intelligence Strategy & Outcomes - Own engineering outcomes across Astra and the CLM cloud-native services for Agents and CIG, including the APIs, integrations, and orchestration capabilities that support them. - Partner with Product and Design leadership to shape strategy, roadmap, sequencing, and investment decisions, using customer feedback and adoption signals to guide priorities. - Translate portfolio priorities into integrated engineering plans, technical investments, clear ownership, and explicit cross-team dependencies. - Define and review measures of customer impact, adoption, business value, delivery predictability, and product quality. ### Converged Platform Architecture & Interfaces - Define architecture direction and a convergence roadmap for shared data models, database services, IAM, Agents, and AI Assistants serving Astra and CLM cloud-native services for Agents and CIG. - Design shared components so they can be consumed consistently across Astra and CLM while allowing product-specific extensions where needed; establish clear service boundaries and ownership across Astra, Agents, CIG, and Core CLM. - Own a unified interface suite across APIs, user interfaces, and MCP servers, with consistent contracts and patterns for Astra and CLM cloud-native Agent and CIG services. - Guide cloud-native APIs and integrations, including MCP and other AI orchestration interfaces for Contract Intelligence workflows and customer systems; ensure services are secure, tenant-aware, scalable, observable, versioned, reliable, and backward compatible. - Improve API, UI, and MCP design standards, documentation, and developer experience for Agiloft teams, customers, and partners. ### Agents, AI Assistants & Contract Intelligence Graph - Partner with Applied AI & Knowledge Engineering to translate AI and knowledge capabilities into production-ready Agents and AI Assistants, service interfaces, architecture, and engineering plans. - Establish practices for evaluating AI-enabled features, including output quality, permissions, privacy, latency, cost, and operational behavior. - Guide engineering evolution of CIG and the services used by Agents and AI Assistants so they provide a dependable foundation for Contract Intelligence experiences across Astra and CLM. - Set appropriate technical guardrails for AI-powered workflows and integrations with external systems. ### Delivery Excellence & Operational Reliability - Own the software development lifecycle across the Contract Intelligence engineering organization, including planning, development, testing, release coordination, and post-release stabilization. - Coordinate dependencies and release plans across Astra, CLM cloud-native services, Core CLM, and other integration teams. - Establish operational measures such as service availability, API latency, error rates, integration reliability, and incident recurrence; use them to drive improvement. - Lead root-cause analysis and remediation for significant incidents and Level 3 technical escalations within the domain. - Balance roadmap delivery with reliability, security, modernization, performance, and long-term technical health. - Be a hands-on technical contributor to the codebase, code reviews, and architecture decisions. ### Organization, Talent & Cross-functional Leadership - Lead managers and technical leaders responsible for Astra, Agents, CIG, and the supporting integration capabilities. - Align Astra team priorities and technical direction with shared components and CLM cloud-native services while maintaining clear accountability for each product outcome. - Develop managers and senior engineers through clear expectations, coaching, delegation, feedback, performance management, and career development. - Build an accountable, collaborative engineering culture with strong technical ownership, predictable execution, and continuous learning across distributed teams. - Recruit, onboard, develop, and retain strong engineering talent; identify capability, capacity, and organizational needs as the portfolio grows. - Partner across Product, Design, Core CLM, Applied AI & Knowledge Engineering, QA, Cloud Operations, Security, and Support to resolve dependencies and deliver customer outcomes. - Communicate technical direction, tradeoffs, delivery risks, and investment needs clearly; serve as an engineering escalation point for complex Contract Intelligence issues. - Other duties as assigned. ### Required Qualifications - BS/MS in Computer Science, Engineering, or equivalent practical experience. - 10+ years of progressive software engineering experience, including 5+ years leading and developing multi-team engineering organizations with managers and senior technical leaders with clear delivery accountability and performance management. - Demonstrated ownership of engineering outcomes for a substantial product portfolio, platform, or technical domain, measured by customer adoption, usage, and business value. - Deep experience with cloud-based SaaS systems, distributed services, APIs, enterprise integrations, and user-facing applications, with sufficient technical fluency to guide architecture and evaluate tradeoffs. - Demonstrated success building shared or converged components used by multiple products or services, including data models, database services, and IAM, with clear interfaces and ownership boundaries. - Hands-on technical fluency in production AI/LLM functionality, Agents, and AI Assistants, including the engineering practices needed to assess quality, privacy, reliability, and cost. - Strong understanding of engineering measures such as delivery predictability, service reliability and SLOs, defect trends, performance, and operational health. - Proven partnership with Product and Design to turn customer priorities into executable engineering roadmaps, including sound build, buy or partner decisions, and to deliver predictability against reliability and quality measure. - Clear communicator who translates technical risks, tradeoffs and decisions into business terms for executives and cross-functional partners. - Sound responsible AI judgement, with experience setting guardrails for accuracy, privacy, security, permissions, transparency and human oversight in customer-facing AI features. - Develops managers and technical leaders, builds succession and helps people do their best work. ### Preferred Qualifications - Experience leading AI-enabled products, Agent architectures, or orchestration interfaces such as MCP. - Experience with knowledge graphs, graph-based systems, or other data-rich enterprise platforms. - Background in CLM, legal technology, workflow automation, or enterprise SaaS. - Experience scaling engineering organizations and operating practices as products grow, including in compliance-driven environments.