About the role

Role: AI Engineer

Own the AI pipeline end-to-end for DeepHow’s production ML stack—turning prototypes into reliable systems real users depend on.

Responsibilities

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

Requirements

3–7+ years shipping ML/AI in production
Strong Python and fluency in PyTorch or TensorFlow
Hands-on experience with LLMs: prompting, fine-tuning, RAG, and evals
Solid MLOps: CI/CD for models, monitoring, and cost optimization
Experience deploying on GCP or AWS (GCP preferred)
Comfort with vector databases, embeddings, and retrieval systems
Startup-speed execution; build-and-ship mindset (not research)

Nice to Have

Multimodal/video/speech AI experience
Experience with tools like MLflow, Kubeflow, Airflow (or similar)
Domain experience in manufacturing or frontline workforce workflows
Background shipping AI features in a SaaS product

About DeepHow

DeepHow is a Physical AI platform for industrial manufacturing, pharmaceuticals, and electronics. It helps organizations capture expert knowledge, convert it into dynamic work instructions, and verify execution on the front line using AI-powered validation and guided workflows.