Head of AI

Uplift People Consulting•San Francisco, CA

3h•Remote

About The Position

Our client is a global leader in AI-driven industrial reliability, helping asset-heavy industries prevent unplanned downtime through advanced diagnostics, predictive maintenance, and actionable insights. Their platform combines industrial expertise, sensor data, and machine learning at scale to deliver measurable ROI for manufacturers worldwide. AI is central to their product strategy and long-term advantage. Role Overview (Fully remote USA) We are seeking an AI leader to define and scale our clients AI/ML strategy, architecture, and execution. In this role, you will turn large-scale industrial data into reliable, explainable, production-ready AI systems that drive real-world impact across manufacturing environments. You will operate at the intersection of AI, industrial systems, product, and business, leading teams that build models customers trust for mission-critical decisions.

Requirements

5 to 10 years of experience in AI/ML, data science, or applied research
Strong hands-on background in: Machine learning for time-series / sensor data Anomaly detection, forecasting, or predictive modeling
Experience deploying ML models into production at scale
Deep understanding of MLOps, model monitoring, and reliability
Proven ability to lead cross-functional teams and influence product direction
Strong communication skills — able to explain complex models to non-technical stakeholders
Fully remote role anywhere from USA

Nice To Haves

Experience in industrial IoT, manufacturing, energy, oil & gas, or asset-heavy industries
Exposure to vibration analysis, condition monitoring, or reliability engineering
Experience with edge AI or hybrid cloud-edge architectures
Familiarity with GenAI / LLMs applied to diagnostics, knowledge systems, or decision support
Prior experience scaling AI in a B2B SaaS environment

Responsibilities

AI Strategy & Vision Define and own the AI roadmap aligned with product and business strategy
Identify opportunities to apply ML, deep learning, signal processing, and GenAI to asset reliability and maintenance workflows
Drive the evolution from predictive prescriptive autonomous decision systems
Model Development & Deployment Lead development of fault detection, anomaly detection, remaining useful life (RUL), and root cause analysis models
Build and deploy production-grade ML systems handling noisy, real-world industrial data
Ensure models are robust, explainable, and trusted by customers in safety-critical environments
Own model lifecycle: experimentation validation deployment monitoring retraining
Data & Platform Leadership Partner with data engineering to scale pipelines for time-series, vibration, acoustic, thermal, and contextual data
Define best practices for MLOps, model observability, drift detection, and performance SLAs
Champion AI architecture choices that scale globally across plants, geographies, and asset classes
Team Building & Leadership Build, mentor, and lead a high-performing team of ML engineers, data scientists, and applied researchers
Establish strong engineering and scientific rigor while balancing speed and impact
Foster collaboration across product, engineering, reliability experts, and customer teams
Product & Customer Impact Work closely with product leadership to translate AI capabilities into customer-facing value
Support enterprise customers in explaining model outputs and building trust in AI-driven recommendations
Partner with sales and customer success on strategic accounts where AI differentiation matters
Innovation & Thought Leadership Stay ahead of advances in industrial AI, time-series ML, edge AI, and GenAI
Evaluate academic research and emerging technologies for real-world applicability
Represent the company as an AI thought leader with customers, partners, and at industry forums