Agiloft - Senior Director, Software Engineering - Contract Intelligence
Requirements
• 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. • 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.
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.
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