S

Shift

AI-driven SaaS for fraud detection

AI Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeParis, FranceNo salary listed

About the Job

Requirements

  • Hands-on expertise with agentic frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI, and advanced retrieval-augmented generation architectures.
  • Experience with Model Context Protocol, tool-calling integrations, and structured large language model outputs.
  • Strong grasp of vector databases, embedding strategies, prompt engineering, and LLMOps including observability, evaluation, and experiment tracking.
  • Deep expertise in the Azure ecosystem.
  • Strong API design principles, including REST, OpenAPI, and API gateways, and experience with containerization using Docker or Podman and Kubernetes.
  • Solid understanding of event-driven architectures, such as Azure Event Hub, Azure Service Bus, or Apache Kafka.
  • High proficiency in Python, strong experience in C# (.NET), Java, C++, or a similar language, and hands-on experience with Infrastructure as Code using Terraform or Bicep.
  • Ability to explain complex architecture clearly to technical and non-technical stakeholders.
  • Proven influencing skills and business acumen to drive initiatives and build consensus across organizational levels.
  • Desire to upskill team members through code reviews and design sessions.
  • Proactive approach to driving work and initiatives.
  • Passion for the field and ongoing awareness of new technologies, best practices, and peer approaches, with the ability to translate relevant practices to Shift.

Responsibilities

  • Define and spearhead the technical architecture for Shift’s agentic AI ecosystem, translating high-level product goals into sustainable, scalable technical implementations.
  • Lead the architecture of backend services required to serve, manage, and scale AI agents in production environments.
  • Partner with agent-focused squads to review technical designs, enforce engineering standards, and ensure alignment with the broader system design.
  • Drive continuous improvements across model performance, output quality, latency, context-window efficiency, and token usage and compute-budget governance.
  • Benchmark and integrate emerging paradigms, such as Model Context Protocol, advanced retrieval-augmented generation patterns, vector pipelines, and agent orchestration frameworks, into the production stack.
  • Establish governance, security, and observability frameworks (LLMOps) to ensure AI deployments are audit-ready, secure, and highly reliable.

About the Company

Shift

AI-driven SaaS for fraud detection

Shift provides AI-driven SaaS solutions for the insurance industry to detect fraud and automate claims processing. Its software analyzes large amounts of data with machine learning models to spot fraudulent activity and to route and accelerate claims through automated workflows. Subscriptions give insurers access to the platform, with additional consulting and support services available. Compared with others in the space, Shift targets a broad range of insurers—from large multinationals to regional providers—focusing on scalable, data-driven automation rather than single-point tools. The company’s goal is to help insurers reduce costs, improve the accuracy of decisions, speed up claims handling, and boost customer satisfaction.

Company size
501-1,000
Company stage
Series D
Total funding
$318M
Headquarters
Paris, France
Founded
2013

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