Senior AI Engineer – Enterprise Data & AI

Zoox is seeking a highly motivated, hands-on AI Engineer to spearhead the end-to-end development and deployment of our Enterprise Gen AI and LLM initiatives. You will serve as the primary technical driver for establishing guard rails, building AI agents and automated workflows that fundamentally transform operations across Procurement, Supply Chain, Legal, Finance, HR and Marketing. This is an engineering-intensive role for a self-starter who thrives on developing, implementing and scaling production-ready AI systems that add business value by solving complex cross-functional challenges.

Design and implement production-grade LLM applications, managing the full stack framework from data ingestion and vector database integration to prompt engineering, fine-tuning and model evaluation.

Build and deploy sophisticated intelligent agents capable of complex reasoning, secure tool usage, and autonomous execution of multi-step business workflows.

Own the deployment process, including self-healing systems, latency optimization, cost management and robust performance monitoring and alerting in a large-scale enterprise environment.

Partner closely with cross-functional teams (Legal, Finance, HR etc.) to identify operational bottlenecks and translate them into efficient, code-driven AI automation.

Implement rigorous standards for security, data privacy and accuracy, ensuring the AI framework to integrate seamlessly with existing corporate infrastructure.

8+ years in Data Engineering, Software Engineering, or Data Science, with at least 2+ years of hands-on experience deploying GenAI/LLMs in a production enterprise environment.

Deep proficiency in Python and frameworks such as LangChain, LlamaIndex, or AutoGen.

Experience with cloud AI services (AWS Bedrock, or Google Vertex AI) and vector databases (Pinecone, Weaviate, Milvus or OpenSearch).

Experience building agents and AI workflows.

Proven ability to translate "business pain" into "technical requirements." 

Previous experience building AI solutions for corporate functions like Finance, Legal (e.g. contract analysis), HR (e.g. policy retrieval) or Supply Chain (e.g. forecasting efficiency)

Familiarity with automated evaluation pipelines like RAGAS, Arize, or other LLM-based evaluation metrics.

Experience with techniques such as quantization, speculative decoding, or efficient fine-tuning (LoRA/QLoRA) to improve model performance and reduce inference costs.

Previous experience in the autonomous vehicle, robotics or high-tech manufacturing sectors.