AI Engineer

AI Engineer

Location:Hybrid/On-site (Bay Area preferred)
Compensation:$150K-$175K plus Equity

Role Overview

We're seeking a hands-on AI Engineer who builds production-ready AI systems, not research prototypes. You'll optimize our AI ingestion pipeline for more accurate, responsive agentic behavior, deploy high-performance models on GPU infrastructure using our Trident architecture, and maintain robust MLOps workflows from training through production deployment. This is for engineers who ship code, not just notebooks.

Key Responsibilities

• Enhance AI Pipeline Accuracy:Improve our data ingestion and processing pipeline to deliver more accurate responses and sophisticated agentic behaviors in production applications.

• GPU-Optimized Model Deployment:Deploy and optimize AI models on high-performance GPU infrastructure using our Trident architecture, ensuring efficient training, inference, and scaling.

• Production MLOps:Build and maintain end-to-end MLOps pipelines including RAG systems, model distillation, fine-tuning workflows, training orchestration, and production inference deployment.

• Data Model Engineering:Design and implement robust data models and processing workflows that power our AI persona capabilities.

• Infrastructure & DevOps:Create production-grade CI/CD pipelines, containerization (Docker), comprehensive logging systems, and monitoring for AI model performance.

• Real Production Deployment:Take AI systems from development through production deployment, focusing on reliability, performance, and operational excellence.

Required Technical Skills

Core Programming (Non-negotiable):

• Python(primary language for AI/ML work)

• Strong proficiency in C++, Java, or C#for performance-critical components

• Data modeling and processingat production scale

AI/ML Production Stack:

• RAG Pipelinedevelopment and optimization

• MLOps workflows:training, inference, model lifecycle management

• Model distillation and fine-tuning techniquesfor production deployment

• Experience deploying models toGPU infrastructure(Trident or similar architectures)

Production Engineering:

• CI/CD pipelinecreation and management

• Docker containerizationand microservices architecture

• Production logging, monitoring, and observability

• Experience scaling AI systems in real production environments

What We DO Want

• 3-5 yearsof production AI/ML engineering experience

• Engineers frommid-sized companieswho have successfully deployed AI systems at scale

• Proven track record of building, deploying, and maintaining ML systems in production

• Experience optimizing AI systems for performance, cost, and reliability

• Strong system design and architecture skills for scalable AI applications