AI Engineer

Ketch-posted 2 months ago
$120,000 - $150,000/Yr
Full-time • Mid Level
Hybrid • San Francisco, CA
51-100 employees
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As an AI Engineer at Ketch, you’ll design and build systems that help users interrogate, explore, and act on AI-driven insights about their data. You’ll architect and scale APIs and data pipelines that classify information and assess risk across massive datasets, documents, and enterprise systems. Blending backend engineering with applied AI, you’ll turn raw data into actionable intelligence, deliver it through fast, reliable services, and create feedback loops that continuously improve performance. You’ll partner with product, design, and customer teams to make advanced AI capabilities feel simple, transparent, and trustworthy within the Ketch platform. This is a hybrid position based in our San Francisco HQ 3 days a week

  • Architect and evolve agentic in-app experiences that enable users to interrogate, explore, and act on data classification insights.
  • Develop scalable data classification pipelines that process documents, metadata, and records across systems using LLMs, embeddings, retrieval-augmented generation, and deterministic heuristics to accurately classify data and flag risks.
  • Implement observability and feedback loops that monitor model performance, enable human-in-the-loop validation, and continuously improve accuracy over time.
  • Develop APIs, services, and systems that extend and enhance our data privacy and permissioning platform.
  • Collaborate with product management and customers to understand customer challenges and translate them into technical solutions.
  • Ensure high code quality and maintain best practices in CI/CD, testing, and deployment pipelines.
  • Proven experience designing and scaling backend systems in Python or Go, building APIs and data pipelines with a strong foundation in system design, testing, CI/CD, and cloud deployment.
  • Experience developing AI systems leveraging large language models, agentic frameworks (e.g., LangChain, PydanticAI), and RAG pipelines for context-grounded reasoning over enterprise data.
  • Familiarity with prompt engineering, fine-tuning, and evaluation methods for improving real-world model performance.
  • Experience instrumenting observability, tracking model metrics, and designing feedback loops for human-in-the-loop validation.
  • Ability to collaborate with product, design, and frontend teams to translate AI capabilities into intuitive user experiences.
  • Demonstrated ability to learn new technologies and apply them in practical contexts.
  • A degree in Computer Science or a related field, or equivalent work experience.
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