Staff AI Engineer: Automate Finance Ops at Scale

T:0; San Francisco (CA); Presencial; USD 180 000 - 260 000; Tempo integral

Resumo da oferta

t0.ai is hiring a Staff/Senior AI Engineer to design and deploy intelligent systems that automate complex financial workflows. You will leverage agentic frameworks, orchestration layers, and pragmatic model selection to deliver reliable production AI at scale.

You will architect AI agents, apply prompting, RAG, fine-tuning, or training from scratch, and collaborate with backend/frontend teams to ensure secure, scalable deployments.

Qualificações

• 5+ years in applied AI/ML engineering with production deployments.
• Strong Python skills and experience with ML frameworks (PyTorch, JAX, TensorFlow).
• Track record of building and deploying AI agents, orchestration frameworks, or multi-step reasoning systems.
• Understanding of monitoring, evaluation, and iteration in production AI systems.
• Ability to balance innovation with reliability, scalability, and compliance.

Responsabilidades

• Architect and deploy AI agents using frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, and emerging OSS stacks.
• Apply the right techniques across the AI spectrum — prompting, RAG, fine-tuning, or training from scratch.
• Design and optimize agent architectures for reasoning, tool use, and workflow automation.
• Build systems that improve with usage, creating compounding AI flywheels.
• Work with backend and frontend engineers to integrate AI reasoning into secure, production systems.

Conhecimentos

AI/ML engineering

Python

ML frameworks

AI agents

Production deployments

Ferramentas

LangChain

LlamaIndex

AutoGen

CrewAI

Semantic Kernel

Descrição da oferta de emprego

t0.ai is hiring a Staff/Senior AI Engineer to design and deploy intelligent systems that automate complex financial workflows. You will leverage agentic frameworks, orchestration layers, and pragmatic model selection to deliver reliable production AI at scale.

You will architect AI agents, apply prompting, RAG, fine-tuning, or training from scratch, and collaborate with backend/frontend teams to ensure secure, scalable deployments.