The Amazon Knowledge Graph (AKG) team is re-inventing knowledge graphs for the LLM-era, developing sophisticated ML models and pipelines that enable efficient LLM grounding and power LLM-based customer experiences.
Requirements
- Experience with data processing and ETL pipelines at scale (Spark, GlueJob, Kafka)
- Experience with Graph Databases like Amazon Neptune, Neo4J, etc., LLM inference optimization, working with scientists
- Experience with building Knowledge Graph and Graph Databases like Amazon Neptune, Neo4J, etc,
- Experience with building agent based on LLM, prompt engineering, and ML model inference optimization
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
Responsibilities
- Develop efficient data processing pipelines to handle large-scale training and inference data
- Support experimentation and A/B testing infrastructure to evaluate model improvements
- Participate in code reviews, technical design discussions, and sprint planning to ensure high quality software delivery
- Develop and optimize LLM-assisted tools that revolutionize knowledge graph creation, from automated ontology generation to real-time fact extraction and verification
- Architect AI/ML systems that power our billion-entity knowledge graph, transforming raw data into intelligent, interconnected information at scale
- Release and maintain ML model infrastructure to enable high-throughput, low-latency inference in production environments
- Collaborate with applied scientists to productionize ML models, and deliver new architectures for knowledge mining and graph construction
Other
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- work safely and cooperatively with other employees, supervisors, and staff
- adhere to standards of excellence despite stressful conditions
- communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service
