AI Software Engineer

Requirements

• 3–5 years of hands-on experience with distributed systems, backend services, or data platforms.
• Experience with event streaming (Kafka, Flink, Kinesis, or similar) and data pipelines (Spark, Databricks, or comparable).
• Familiarity with production operations: Kubernetes, CI/CD, monitoring, alerting, and incident response.
• Strong engineering fundamentals (Java preferred; Python familiarity a plus).
• A pragmatic approach: caring about correctness, performance, reliability, and cost.

Nice to Have

• Scala; Jenkins/Argo or similar CI/CD tooling; observability tools (Prometheus, Grafana, Splunk); experience with high-traffic systems; prior exposure to LLM APIs or AI-powered services in production.

What You'll Be Doing

• Designing and developing components and subsystems within the data platform, taking on features of moderate complexity.
• Working across multiple domains and modules — debugging integration issues, recognizing dependencies, and understanding how your work fits into the larger system.
• Writing quality code that meets team standards, owning test plans for your features, and deploying and validating your work in production.
• Collaborating with product management, design, and data science to turn requirements into testable solutions.

Perks and Benefits

• Work on a stable, widely used product, not a greenfield experiment.
• Operate real systems with real customers and real consequences.
• Grow rapidly across domains, with increasing independence and impact.