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# AI Engineer

- **Company:** Jobot
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $225,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Programming Tools, Distributed Systems, Python (Programming Language), AI Infrastructure, Large Language Models, Multi-Agent Systems, Backend, Event Driven Architecture, Low Latency, Front End Software Development
- **Published:** August 1, 2026
- **Apply:** https://www.dice.com/job-detail/977ad498-3b2e-41c9-9be8-2c6244b7c9b5

## About the Role

* 4+ years of experience building and shipping production software systems, at least 2 years of AI engineering experience
 * Strong backend engineering fundamentals with deep experience in Python
 * Proven experience building AI-native products, agentic systems, LLM workflows, RAG pipelines, or orchestration infrastructure in production environments
 * Strong understanding of distributed systems, async processing, event-driven architectures, and scalable backend design
 * Experience working with modern AI tooling/frameworks such as LangGraph, LangChain, vector databases, workflow engines, or model serving infrastructure
 * Ability to move quickly in ambiguous 0 1 startup environments with high ownership and autonomy
 * Strong product instincts with an interest in building practical AI systems that solve real operational problems
 * Comfortable operating across the stack when needed, including APIs, frontend integrations, internal tooling, and user-facing workflows
 * Passion for AI infrastructure, automation, developer tooling, and the future of intelligent software systems

## Description

What You'll Work On

 * Building and scaling agentic AI systems that autonomously execute complex, multi-step workflows end-to-end
 * Designing AI orchestration layers, agent harnesses, tool calling frameworks, and execution pipelines for production LLM systems
 * Developing and deploying LLM-powered infrastructure including retrieval pipelines, memory/context systems, evaluation layers, and workflow automation
 * Building scalable backend services, APIs, and distributed event-driven architectures that support high-throughput AI workloads
 * Creating production-grade AI systems focused on reliability, latency, observability, and iteration speed
 * Designing integrations with external platforms, APIs, and third-party services to expand agent capabilities
 * Improving AI system quality through evaluation frameworks, prompt iteration, retrieval optimization, and human-in-the-loop workflows
 * Collaborating closely with product and engineering teams to rapidly prototype and ship new AI-native product experiences
 * Evolving platform architecture to support rapid growth, increasing workflow complexity, and new AI capabilities over time


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