Senior Software Engineer

At Zendesk, our focus is helping our customers build great relationships with their customers.

We are looking for a Senior Software Engineer to join our AI Copilot organisation. AI Copilot is a multi-million ARR product that puts AI directly into the hands of customer service agents and administrators.

Responsibilities

• Own and deliver end-to-end product features , from technical design through implementation, testing, release, and iteration.
• Work closely with ML Scientists and ML Engineers to integrate ML-powered capabilities into the product as reliable, user-facing features.
• Contribute to technical design discussions and architecture decisions within your team, with growing influence across teams.
• Build and maintain scalable backend services, APIs, and integrations that support AI-powered product experiences.
• Collaborate with Product Management, Design, and other engineering teams to turn product requirements into well-scoped technical work.
• Improve the reliability, performance, and maintainability of the systems you work on , proactively identifying and addressing technical debt.
• Mentor more junior engineers through code review, pairing, and knowledge sharing.

Requirements

• 5+ years of experience building and operating large-scale, Internet-facing applications.
• Fluent in Ruby; working proficiency in Python is a big plus.
• Solid experience with distributed systems, RESTful APIs, and cloud infrastructure (AWS).
• Experience with containerised deployments (Docker, Kubernetes) and CI/CD practices.
• A track record of owning features end-to-end and delivering them to production with high quality.
• Ability to work with uncertainty and the flexibility to pivot with changing priorities.
• Comfort working in a team alongside ML engineers and scientists , you don't need to be an ML expert, but you should be curious and able to collaborate effectively across disciplines.
• Strong collaboration skills and a genuine desire to help the team succeed.

Preferred

• Experience with SQL and data infrastructure (Snowflake, dbt) , understanding data pipelines and transformations is valuable in our environment.
• Exposure to LLM integration patterns: prompt engineering, evaluation, monitoring, or multi-provider setups.
• Familiarity with ML pipeline tooling (e.g., Metaflow) or ML frameworks.
• Experience with event-driven architectures (e.g., Kafka).
• Experience with iterative, metrics-driven product development (A/B testing, feature flags, incremental rollouts).

Tech Stack

• Our code is written in Ruby and Python
• Our servers live in AWS
• Our ML pipelines use Metaflow
• Our data is stored in S3, RDS MySQL, and Snowflake (with dbt for transformations)
• Our services and models are deployed to Kubernetes using Docker