Artificial Intelligence Engineer

Role Overview

You’ll own DeepHow’s AI pipeline end-to-end, taking over and hardening the production ML stack. This is a build-and-ship role focused on production deployment and iteration—turning prototypes into reliable systems that real users depend on.

Responsibilities

• Own the AI pipeline: ingestion, processing, inference, and monitoring
• Deploy and scale LLM, VLM, and speech models in production on GCP
• Optimize latency, cost, and reliability across the stack
• Build RAG pipelines, prompting, and evaluation frameworks
• Develop infrastructure and tooling to accelerate experimentation and shipping
• Lead MLOps and production deployment from day one

Requirements

• 3–7+ years shipping ML/AI in production
• Strong Python
• Fluent with PyTorch or TensorFlow
• Hands-on experience with LLMs: prompting, fine-tuning, RAG, and evals
• Solid MLOps experience including model CI/CD, monitoring, and cost optimization
• Experience deploying on GCP or AWS (GCP preferred)
• Comfort with vector databases, embeddings, and retrieval systems
• Ability to execute quickly in a startup environment

Nice to Have

• Video, speech, or multimodal AI experience
• Experience with MLflow, Kubeflow, Airflow (or similar)
• Manufacturing or frontline workforce domain experience
• Experience shipping AI features in a SaaS product

Location / Work Mode

• Remote (Preferred: Dallas, United States)