Job Title: AI Engineer Employment Type: Full-time Location: Remote (Preferred Dallas) ________________________________________ About DeepHow DeepHow is a Physical AI platform for industrial manufacturing, pharmaceuticals, and utilities that helps organizations capture expert know-how, turn it into dynamic work instructions, and drive verified execution on the front line. The platform spans knowledge capture and sharing, AI-powered verification through Smart Compare and photo/video validation, and time and motion intelligence through guided workflows, SOP adherence, and real-time execution visibility. DeepHow supports customers from knowledge capture to verified execution, with strategic account expansion often centered on verification, AI-guided workflows, and time and motion intelligence. The Role We’re looking for an AI Engineer to own our AI pipeline end-to-end. You’ll take over our production ML stack, harden it, and ship improvements fast. Day one, your focus is MLOps and production deployment making sure our models are fast, reliable, and cost-efficient at scale. This is a build-and-ship role, not research. If you like turning prototypes into production systems that real users depend on, read on. What You’ll Own • Our AI pipeline — ingestion, processing, inference, monitoring • Deployment and scaling of LLM, VLM, and speech models in production (GCP) • Latency, cost, and reliability optimization across the stack • RAG pipelines, prompting, and evaluation frameworks • Infrastructure and tooling to accelerate experimentation and shipping Education & Experience • Bachelor’s or master’s degree in computer science, Engineering, or a related technical field (or equivalent practical experience) • 3–7+ years shipping ML/AI in production • Strong Python; fluent in PyTorch or TensorFlow • Hands-on with LLMs — prompting, fine-tuning, RAG, evals • Solid MLOps chops: CI/CD for models, monitoring, cost optimization • Experience deploying on GCP or AWS (GCP preferred) • Comfort with vector DBs, embeddings, and retrieval systems • Startup-speed execution Nice to Have • Video, speech, or multimodal AI experience • MLflow, Kubeflow, Airflow, or similar • Manufacturing or frontline workforce context • Background shipping AI features in a SaaS product Why DeepHow • Real AI problems with real users, not demos • Direct impact on frontline workers and industrial operations • Small team, high ownership, fast cycles • Shape the AI roadmap from the ground up