Stratus

• Own a portfolio of commercial mid-market accounts end-to-end, serving as the primary point of contact and trusted advisor. • Build and maintain multi-threaded relationships across champion, economic buyer, and executive levels. • Develop and execute tailored success plans aligned to each customer’s strategic goals and key business outcomes. • Proactively monitor account health, engagement signals, and product usage to identify risk and opportunity early. • Drive on-time renewals across your book of business, managing the full renewal cycle from 90-day forecast through execution. • Maintain a rolling renewal forecast with high accuracy and visibility in Salesforce or equivalent CRM. • Identify and mitigate churn risk through proactive outreach, business reviews, and escalation management. • Achieve gross revenue retention (GRR) targets and net revenue retention (NRR) targets. • Identify and qualify expansion opportunities (upsell, cross-sell, seat expansion) within your existing customer base. • Lead commercial conversations with customers, partnering with your aligned Account Executive to close expansion deals. • Develop account growth plans that outline the path to expansion over a 12–24 month horizon. • Conduct structured Quarterly Business Reviews (QBRs) with key stakeholders, presenting ROI, product usage insights, roadmap alignment, and forward-looking success plans. • Build executive sponsorship programs and escalate appropriately when executive relationships need reinforcement. • Monitor product adoption milestones and proactively intervene when customers fall behind adoption benchmarks. • Serve as the voice of the customer internally, advocating for product enhancements, escalating support issues, and sharing market feedback with Product and Engineering. • Collaborate with Sales, Marketing, and Implementation on account strategy, case studies, and customer advocacy programs.

• Owner of AI and data product initiatives end to end. • Build agentic AI and LLM-powered features, from concept to shipped product. • Turn large construction data sets into commercial products. • Create crisp specs and PRDs for engineering execution. • Maintain a working feedback loop with design partners. • Partner with PMM, sales, and CS for feature adoption.

• Owns the Stratus Intelligence Platform: the data, machine learning, and reasoning capabilities that make compounding intelligence real. • Accountable for delivering in the AI roadmap, ensuring that AI workloads scale on the platform, and the ML lifecycle that trains and serves models. • Set the Intelligence strategy in close partnership with the CTO, build the team that delivers it, and stay close enough to the technical work to make hard architectural calls when they arise. • Stand up the canonical data layer, the ML platform, and the learning and reasoning capabilities. • Hire and develop a high-leverage Intelligence team (Data, AI/ML, AVE). • Set the bar for craft and shape the operating cadence. • Stand up the trust patterns needed to ship AI workloads to customers with confidence: evaluation frameworks, observability for AI, drift detection, confidence scoring.

• Build foundation models and generative AI tools alongside a team of technologists. • Design and build agentic workflows — multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen), tool use, multi-step planning, and human-in-the-loop checkpoints — to automate complex engineering tasks. • Establish evaluation, guardrails, and failure-mode analysis for agent systems to ensure they are safe, reliable, and production-ready. • Develop scalable data pipelines for diverse data sources used in production ML systems, including BIM, CAD, and infrastructure design data. • Work with large-scale, multi-modal datasets — including text and geometric data — to design novel preprocessing, augmentation, analysis, and content understanding. • Transform unstructured infrastructure and design data into representations suitable for machine learning. • Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs. • Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments. • Architect and optimize pipelines for scalability, reproducibility, and cloud deployment. • Mentor junior engineers and provide technical guidance on complex data challenges. • Drive technical decision-making and influence best practices across the team. • Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives. • Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation. • Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs. • Participate in technical planning and roadmap development.

• Build the customer-facing AI layer for Stratus — the production agent systems, tool integrations, and evaluation infrastructure that let our products reason over MEP fabrication data and act on a contractor's behalf. • Design and ship multi-agent workflows, build the tool and context layer that connects agents to Stratus data, and own the guardrails, evals, and observability that make those systems safe and trustworthy in front of customers. • Build and maintain the Revit-side integrations that feed the AI layer — the add-ins and publishing paths that move design and fabrication data out of our customers' Autodesk Revit environment and into Stratus. • Use AI-assisted development tooling (Claude Code, Cursor, Copilot, etc.) as a first-class part of the dev loop — writing tests for AI-generated changes and exercising clear judgment about when AI output is ready to ship. • Collaborate with product managers, designers, and customer-facing teams to scope, design, and ship — grounding technical decisions in real design and fabrication workflows. • Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs. • Share knowledge and raise the engineering bar through code review and pragmatic best practices.

• Own the architectural vision and technical roadmap for Intelligence (data, learning, reasoning) and Agentic interfaces, workflows • Design and evolve agent-based workflows, enabling reasoning-capable, adaptive, multi-step systems with strong trust patterns (grounding, evals, confidence scoring) • Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness • Make AI workloads observable to engineering and product • Architect for simplify the core, customize at the edge • AI/ML capabilities are opinionated at the center, with foundations for marketplace or platform-exchange patterns at the edge • Architect agentic UX and enable just-in-time interfaces • Build evaluation frameworks: model quality, agent reliability, drift detection, eval harnesses, observability for AI workloads • Set engineering standards through working reference implementations, in-repo templates, and patterns AI/ML teams can copy • Lead the polyglot AI stack: vector search, embeddings pipelines, RAG architectures, agent frameworks, model lifecycle, MLOps • Drive AI-augmented engineering practice • Personally set the bar • Engage directly with customer-facing teams and customers to ground AI architectural decisions in real workflows • Mentor engineers; raise the technical bar through code review, design review, and technical coaching.

• Own our canonical data architecture — the schema, contracts, tenancy, and governance. • Make production data AI-ready: well-modeled, contract-enforced, lineage-tracked, and drift-detectable. • Own the canonical data model — the normalized definition of the core business objects shared across our products. • Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution. • Lead staged modernization toward the right mix of stores and patterns. • Own the architectural direction of the data pipeline and lake / lakehouse layer. • Drive hands-on prototypes, reference implementations, and in-repo guardrails. • Partner with database engineering on production data health while owning long-term architectural direction.

• Own product strategy and roadmap for an assigned pillar • Manage the full product lifecycle from define to measure • Coordinate with peer PMs for cross-pillar features • Partner with PMM, sales, and CS for successful launch and adoption • Communicate effectively with executives and customers

• Ship features and customer-facing enhancements across the full stack, working from product specs and customer feedback. • Investigate and fix customer-reported bugs in your area of the product — performance regressions, edge cases, integration breakage — with the self-sufficiency to dive into unfamiliar code and deliver a fix. • Use AI-assisted development tooling (Claude Code, Cursor, etc.) as a first-class part of the dev loop — including writing tests and evals for AI-generated changes, and exercising judgment about when AI output is shippable. • Contribute to monolith decomposition and modernization as touch points emerge in your work. • Implement and maintain modern automated testing — unit, integration, and AI-component evals. • Collaborate with cross-functional teams to define, design, and ship; coordinate deployments and incident response with peers. • Advocate for incremental delivery and contribute to the engineering culture that surrounds you.

• Collaborate with cross-functional teams to define, design, and ship new product surfaces emerging from Labs. • Drive architectural decisions and implementations for the team and the platforms it produces. • Design and build production-grade agentic workflows on top of what we ship. • Lead technical standards and best practices that impact multiple teams. • Mentor and guide engineers and influence engineering culture. • Make significant individual contributions to our SaaS product across both Azure and AWS. • Help modernize and consolidate legacy surfaces as integration touch points emerge. • Ensure the performance, quality, and responsiveness of customer-facing applications and the agentic systems built on top of them. • Implement and maintain modern automated testing practices, including evals for AI components. • Advocate for incremental delivery, consistently aligning with the practices of modern high-performing engineering teams.