Job role: AI Engineer
Location: On-site (Bay Area)
Fulltime opportunity
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 processing at production scale
AI/ML Production Stack:
RAG Pipeline development and optimization
MLOps workflows: training, inference, model lifecycle management
Model distillation and fine-tuning techniques for production deployment
Experience deploying models to GPU infrastructure (Trident or similar architectures)
Production Engineering:
CI/CD pipeline creation and management
Docker containerization and microservices architecture
Production logging, monitoring, and observability
Experience scaling AI systems in real production environments
What We DO Want
3-5 years of production AI/ML engineering experience
Engineers from mid-sized companies who 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
Sample Projects You'll Own
Optimize our RAG pipeline for improved accuracy and response quality
Deploy and scale transformer models on our Trident GPU architecture
Build MLOps workflows for continuous model training and deployment
Design data processing systems for multi-modal AI persona training
Create monitoring and alerting systems for production AI model performance
Never miss an opportunity! Create an alert based on the job you applied for.
