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Shift

AI-driven SaaS for fraud detection

Lead AI Engineer - ML/LLM Ops & Agentic AI

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listedRemote

About the Job

Requirements

  • Proven experience building production agentic systems using frameworks such as Microsoft Agent Framework, OpenAI Agents SDK, PydanticAI, LangGraph, or custom solutions, with strong command of tool calling, MCP, retrieval-augmented generation, and evaluation.
  • High proficiency in Python and hands-on experience with experiment tracking, model registry, serving, and monitoring; Databricks and MLflow experience is preferred within the stated qualification.
  • Experience building backend services on Azure, Kubernetes, Terraform, and event-driven patterns.
  • Solid machine-learning foundations and the ability to critically assess new approaches.
  • High proficiency in Python and strong experience with object-oriented programming; ability to design robust, maintainable software systems, write clean code, and apply appropriate design patterns and software engineering best practices.
  • Experience leading technical initiatives across stakeholders and coaching or managing technical contributors, or clear motivation to do so.
  • English fluency is required.

Responsibilities

  • Design and ship production-grade agentic workflows using LLM orchestration, tool calling, MCP, retrieval-augmented generation, and structured outputs; define reference architectures determining which tasks models handle and which deterministic code handles.
  • Own LLMOps standards, including evaluation harnesses, prompt and agent versioning, OpenTelemetry tracing, cost and token governance, release gates, and tenant-aware patterns that preserve client data isolation.
  • Serve as technical owner of the Databricks-based machine-learning platform, including Unity Catalog, MLflow, CI/CD to serving on AKS, and monitoring, and extend it to support the agent and prompt lifecycle.
  • Partner with Cloud Ops to design and deploy architecture for internally self-hosted open-weight foundation models; own the inference layer and model optimizations for latency and throughput, and collaborate on GPU resource provisioning and cluster management.
  • Run time-boxed proofs of concept on emerging techniques, such as relational foundation models for claims fraud detection or agentic ingestion of medical billing sources to generate claim edits, with clear success criteria and a path to production.
  • Coach and develop 2 to 5 people, raise technical standards through design and code reviews, and partner with product, subject-matter experts, architecture, security, and site reliability engineering.
  • Work directly with software engineers and product leaders to design, prototype, and ship AI systems into production, and align priorities and best practices with the Data Science team.

Desired Qualifications

  • Exposure to graph machine learning, fraud, or claims data.
  • French language skills.

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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