Senior AI Engineer (Evisort Agentic Dev)
Posted Updated
Description
- As a Senior AI Engineer, your primary focus will be building AI features and intelligent agents specifically for the Contract Lifecycle Management (CLM) domain, though you will also be expected to contribute to other areas of the product as needed
- While AI development is the primary deliverable, this is first and foremost a software engineering role requiring deep expertise in backend architecture and distributed systems
- You will be responsible for architecting advanced LLM-driven solutions and agentic workflows that are not only innovative but also enterprise-grade—meaning they are highly available, scalable, and secure
- By leveraging Workday’s rich datasets and computing resources, you will build the robust infrastructure necessary to deliver transformative AI value to our customers at scale
- In addition to contributing to feature and service development, you must have an approach of continuous improvement, passion for quality, scale, and security
- 8+ years of professional experience in software engineering and product-building, with a proven track record of shipping and maintaining production code at scale
- 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 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)
- Thrives in Ambiguity: Highly autonomous builder capable of taking open-ended product goals and breaking them down into concrete, scalable engineering realities
- 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)
- 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)
- Familiarity with full-stack development - building web UIs (e.g., React, TypeScript) and backend services/REST APIs in Python (e.g., FastAPI, Django)
- 3+ years of hands-on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user-facing enterprise products
- Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field
- 4+ years of experience optimizing application performance, specifically tackling product constraints such as API latency, token management, and user interaction design
- Technical Leadership & Mentorship: Proven track record of technically leading engineering workstreams, taking ownership of the development lifecycle, and mentoring junior-to-mid level engineers
- 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
- 1–2+ years of hands-on experience building with modern AI orchestration frameworks (e.g., LangChain, LlamaIndex, or multi-agent frameworks) and implementing advanced prompt engineering techniques to manage complex agentic workflows
- 4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP) to deploy highly responsive, scalable systems
- Hands-on experience with production observability and monitoring tooling (e.g., Prometheus/Grafana, OpenTelemetry, Sentry, Datadog) to debug, trace, and measure live systems
- 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)
- Collaborative Communication: Excellent interpersonal skills, with a knack for bridging the gap between product management, design, and foundational ML infrastructure teams
Skills
- Agentic AI
- AI
- API
- Argo CD
- AWS
- Cloud
- Cloud Native
- Contract Lifecycle Management
- Datadog
- Deep Learning
- Distributed Systems
- Django
- Docker
- FastAPI
- GCP
- GitOps
- Grafana
- Helm
- Infrastructure as Code
- Interaction Design
- Kafka
- Kubernetes
- LangChain
- LlamaIndex
- LLM
- Machine Learning
- Observability
- OpenTelemetry
- PostgreSQL
- Prometheus
- Prompt Engineering
- Prototyping
- Python
- RDBMS
- React
- Redis
- REST
- Sentry
- SonarQube
- SQS
- Terraform
- TypeScript