Software Engineer optimizing infrastructure for the leading Gen AI platform, collaborating cross-functionally and ensuring efficiency and resilience in distributed systems.
Responsibilities
Build and scale core infrastructure ā design and optimize distributed systems and APIs that handle millions of real-time queries with low latency and high reliability.
Develop data-intensive systems ā work across SQL, NoSQL, time-series, and object stores, ensuring data pipelines and lookups are optimized for throughput and efficiency.
Optimize performance at scale ā profile and tune systems for latency, throughput, and cost, ensuring the platform can grow with customer demand.
Work on real-time serving systems ā design high-throughput caching layers and data lookup services to enable fast, reliable access to large-scale datasets.
Build and extend pub-sub and orchestration frameworks ā leverage and improve systems like Kafka, RabbitMQ, and Celery to coordinate workloads and streaming data pipelines.
Design internal developer tooling ā create load testing, benchmarking, and performance analysis tools that help us and our customers understand and trust the system.
Collaborate cross-functionally ā partner with product, research, and application engineering teams to ensure the platform can support new capabilities, models, and workloads.
Requirements
6 years of experience
Experience with large scale distributed systems
Experience with NoSQL databases and time-series databases
Worked on real-time high throughput caching systems
Excellent python programming skills
Worked with raw-data lookup indexes such as Lucene and/or similar frameworks
Experience with runtime orchestration and pub-sub frameworks like RabbitMQ, Celery, Kafka
Built low-latency data lookup APIs
Done extensive performance optimizations
Built internal tooling foundations for performance testing, load testing and benchmarking
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