Want to build the "plumbing" that powers not just analytics, but the next generation of AI applications? ️
What do we offer:
Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution
Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering
Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against
Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice
Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence
Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success
Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team
Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems
AI-driven leadership posture: you personally set the bar for AI-augmented practice — you build with AI tools, expect your team to, and know how to distinguish what's ready to ship from what still needs a human call
10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company
Demonstrated track record of building and leading AI/ML or data teams of 5–15 people, with a strong hiring track record in the AI/ML market within the last two to three years
Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning, evals)
Experience shipping production ML and AI workloads to enterprise customers with the trust patterns that come with it: evals, observability, drift detection, confidence scoring
Excellent communication across all audiences — engineers, product, executives, and customers; strong cross-functional partnership instincts with product, engineering, and customer-facing teams
Experience in construction tech, MEP, BIM, AEC, or other CAD and engineering workflow domains
Background in AI security and threat modeling: prompt injection, data exfiltration, agent abuse, tenant isolation for AI workloads
Experience with Azure-native AI architecture (Azure ML, Azure AI Foundry, AKS)
Prior experience at a Series B or growth-stage company navigating the transition from product-market fit to scale
Background in regulated or enterprise sales motions where compliance, security, and SLA discipline are non-negotiable
