Director, Data & AI/ML

Stratus

1d•Remote

About The Position

The Director of Data and AI/ML owns the Stratus Intelligence Platform, which includes the data, machine learning, and reasoning capabilities that enable compounding intelligence. This role is responsible for executing the AI roadmap, ensuring AI workloads scale on the platform, managing the ML lifecycle for training and serving models, and developing learning and reasoning capabilities to derive customer value from data over time. This is a player-coach leadership position requiring both team building and execution. The Director will set the intelligence strategy in partnership with the CTO, build the delivery team, and remain technically involved to make critical architectural decisions. The core principle is compounding intelligence, where each solution and data point enhances the data substrate, the models learning from it, and the reasoning capabilities customers depend on, thereby building Stratus's defensible AI moat.

Requirements

10+ years of professional experience in AI/ML, data engineering, or data science.
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, data engineering, or data science teams of 5-15 people from a small base.
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).
Experience shipping production ML and AI workloads to enterprise customers with associated trust patterns (evals, observability, drift, confidence).
Hands-on player-coach posture, comfortable reviewing technical designs, participating in architecture debates, and writing reference implementations.
Strong hiring track record, with experience building an AI/ML team in the last two to three years.
Excellent written and verbal communication skills, capable of explaining AI/ML strategy to diverse audiences.
Strong cross-functional partnership instincts, with experience working closely with product, engineering, and customer-facing teams.
Experience with multi-tenant data architecture and the operational realities of serving ML and AI workloads to enterprise customers.
Candidates must be based in the U.S.

Nice To Haves

Experience in construction tech, MEP, BIM, AEC, or other CAD and engineering workflow domains (or strong willingness to ramp on the domain).
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).
Experience standing up a data platform or ML platform from early-stage to scale.
Prior experience in 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.

Responsibilities

Partner with the CTO and leadership to set the intelligence strategy and roadmap, and lead its execution.
Build, hire, and develop the Intelligence team, setting standards for craft and shaping the operating cadence, managing both people and agents.
Establish the canonical data substrate for AI/ML workloads, ensuring schema discipline, tenancy isolation, data contracts, lineage, and governance.
Set up the ML and AI platform, including model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practices.
Lead the learning and reasoning capabilities of the platform, focusing on RAG architectures, agentic data systems, knowledge graphs, and patterns for compounding data intelligence.
Develop and drive evaluation frameworks for model quality, agent reliability, drift, and platform effectiveness, making AI workloads observable to engineering, product, and customer success.
Partner with product management to define the AI use case portfolio, translating business needs into ML/AI capabilities and AI possibilities into product opportunities.
Determine the build-versus-buy strategy for the AI/ML stack, prioritizing proven solutions and building only differentiating components.
Collaborate with the platform team to set production readiness standards for AI workloads.
Engage with customer-facing teams and customers to align Intelligence decisions with real-world workflow challenges.
Mentor engineers and data scientists, raising the technical bar through design reviews, code reviews, and technical coaching, and personally setting the standard for AI-augmented practice.