Staff AI Engineer – Architecture Skills

Shift Technology

. Design, prototype, and build production-ready agentic workflows, custom orchestration layers, and tool-calling integrations .

Posted 11/3/2026full-timeRemote • FranceLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building production-ready AI workflows and microservices, with a strong focus on scalability, low-latency integration, and advanced AI frameworks. Proficient in Python and experienced in collaborating with cross-functional teams to implement robust AI systems.

Highest-signal resume keywords

Python ProgrammingMicroservices ArchitectureModel Context Protocol (MCP)Agent FrameworksAzure Cloud Services

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Hard Skills

Software EngineeringObject-Oriented Programming (OOP)REST/OpenAPI DevelopmentPrompt EngineeringLatency OptimizationToken Budget GovernanceExperiment TrackingAI Model BenchmarkingTool/Function CallingVector Databases

Soft Skills

MentoringClear CommunicationCollaborationProblem-SolvingProactive Building

Tools & Technologies

DockerKubernetesCI/CDTerraformBicepAzure Service BusKafka

Industry Keywords

GenAILLMEvent-Driven ArchitectureCustom OrchestrationAI Systems

Tech Stack

Tools & technologies

AzureCloudDockerKafkaKubernetesMicroservicesPythonTerraform

About the role

Key responsibilities & impact

Design, prototype, and build production-ready agentic workflows, custom orchestration layers, and tool-calling integrations
Move AI prototypes into robust, production-grade microservices on Azure
Ensure AI components are scalable, low-latency, and integrated into the broader technology stack
Experiment with and integrate Model Context Protocol (MCP), advanced RAG patterns, vector pipelines, and structured LLM outputs
Establish operational frameworks for experiment tracking, evaluations, prompt engineering, latency optimization, token budget governance, and model observability
Mentor agent-focused engineering squads
Lead technical design reviews and set coding standards
Benchmark new models, AI tools, and architectures and translate breakthroughs into practical implementations
Work with data scientists, software engineers, and product leaders to design, prototype, and ship AI systems into production

Requirements

What you’ll need

Ingénieur·e IA principal·e expérimenté·e et opérationnel·le, disposant d’une expertise pratique approfondie des écosystèmes modernes de GenAI/LLM et de solides compétences en software engineering backend
Expérience avérée de frameworks agentiques tels que Microsoft Agent Framework, LangGraph, LlamaIndex, OpenAI Agents SDK ou des frameworks d’orchestration personnalisés
Expérience pratique du Model Context Protocol (MCP), du tool/function calling, des sorties structurées de LLM et de pipelines RAG complexes
Expérience des frameworks d’évaluation, du prompt engineering, du tracing, de l’optimisation de la fenêtre de contexte et de la gouvernance du compute et des tokens en production
Très bonne maîtrise de Python, indispensable pour travailler dans l’écosystème de l’IA
Solide expérience de la programmation orientée objet (OOP)
Expérience de la conception et de la mise à l’échelle de microservices backend et d’endpoints REST/OpenAPI dans des environnements cloud d’entreprise, notamment Azure
Connaissance des bases de données vectorielles et relationnelles, ainsi que des architectures event-driven telles qu’Azure Service Bus et Kafka
À l’aise avec Docker, Kubernetes, la CI/CD, les méthodologies de test et l’Infrastructure as Code avec Terraform ou Bicep
Profil de builder proactif, capable de transformer des idées ambitieuses en code fonctionnel
Capacité à expliquer clairement des concepts et architectures d’IA complexes à des interlocuteurs techniques et non techniques
Capacité à expérimenter de nouveaux modèles, bibliothèques et articles de recherche afin d’en tirer une valeur produit
Capacité à collaborer, accompagner les équipes, pratiquer le pair programming, réaliser des code reviews et mener des discussions pragmatiques sur la conception

Benefits

Comp & perks

Flexible remote and hybrid working options
Competitive Salary and a variable component tied to personal and company performance
Multiple Learning and Development opportunities, including Focus Fridays, a half-day each month to focus on learning and personal growth
Generous PTO and paid holidays
Mental health benefits
2 MAD Days per year (Make A Difference Days for paid volunteering)
Additional benefits may be offered by country, based on eligibility