Artificial Intelligence Engineer

Job Title: AI Engineer

Employment Type: Full-time

Location: Remote (Preferred Dallas)

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About DeepHow

DeepHow is a Physical AI platform for industrial manufacturing, pharmaceuticals and Electronics 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