Senior AI Engineer (Evisort AI)

Evisort, a leading AI-powered contract management platform, as a Senior AI Engineer. In this role, you will develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis. You will collaborate with other engineers to deliver AI solutions across Workday's product ecosystem and utilize software and data engineering stacks to enable training, deployment, and lifecycle management of various AI pipelines. You will also have the opportunity to develop and deploy new products at scale and leverage Workday's vast computing resources on rich datasets to deliver transformative value to our customers.

Prendre en charge le développement de l'ensemble du cycle de vie, et encadrer les ingénieurs de niveau junior à intermédiaire.

About Workday

Collaborer avec d'autres ingénieurs pour fournir des solutions d'IA à travers l'écosystème de produits de Workday et utiliser des piles d'ingénierie logicielle et de données pour permettre la formation, le déploiement et la gestion du cycle de vie de divers pipelines d'IA.

Développer et déployer de nouveaux produits à grande échelle et tirer parti des vastes ressources informatiques de Workday sur des ensembles de données riches pour offrir une valeur transformative à nos clients.

Requirements

• Collaborative Communication: Excellent interpersonal skills, with a knack for bridging the gap between product management, design, and foundational ML infrastructure teams
• Familiarity with full-stack development - building web UIs (e.g., React, TypeScript) and backend services/REST APIs in Python (e.g., FastAPI, Django)
• Technical Leadership & Mentorship: Proven track record of technically leading engineering workstreams, taking ownership of the development lifecycle, and mentoring junior-to-mid level engineers
• Familiarity with data persistence and async processing - relational databases and schema migrations (e.g., PostgreSQL), caching (e.g., Redis), and message/queue or task workflows (e.g., Celery, Kafka, SQS)
• Thrives in Ambiguity: Highly autonomous builder capable of taking open-ended product goals and breaking them down into concrete, scalable engineering realities
• 3+ years of experience integrating AI capabilities (LLMs, Foundation Models) into enterprise products, framed within the context of robust software service extensions
• 1-2+ years building with AI orchestration frameworks (e.g., LangChain, LlamaIndex) with a focus on creating predictable, reliable, and maintainable agentic workflows
• A track record of writing well-tested, maintainable production code with attention to code quality, security scanning, and dependency hygiene (e.g., unit/integration tests, SonarQube, dependency tooling)
• Product-First AI Mindset: Deep focus on business value, user experience, and applying deep learning or large models directly to solve practical end-user challenges
• Hands-on experience with production observability and monitoring tooling (e.g., Prometheus/Grafana, OpenTelemetry, Sentry, Datadog) to debug, trace, and measure live systems
• System Design & Reusability: Proven ability to architect robust application layers that wrap around AI models, establishing reusable patterns for system predictability, error handling, and seamless UX integration
• 4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP, Azure) to deploy highly responsive, scalable systems
• Experience using feature-flag/experimentation platforms (e.g., LaunchDarkly) to safely roll out, A/B test, and decommission changes, including flag lifecycle and cleanup discipline
• Experimentation & Evaluation: Skilled in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics (e.g., assessing retrieval quality and agentic behavior)
• 2+ years of hands-on experience deploying and operating services in containerized, cloud-native environments (Docker, Kubernetes), with exposure to infrastructure-as-code and GitOps tooling (e.g., Terraform, Helm, ArgoCD)
• 8+ years of professional software engineering experience, with a heavy emphasis on backend architecture, distributed systems, and a proven track record of shipping high-availability production code at scale. Strong fundamental software engineering skills (system design, scalability, and performance optimization) are a non-negotiable primary qualification for this role

Preferred

• Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field