Responsibilities
- Design and deploy backend systems and data workflows that power sophisticated AI applications such as large language models, retrieval-augmented generation, and agent-based architectures.
- Develop high-throughput APIs and microservices to connect AI models with scientific platforms and end-user applications.
- Create and maintain scalable data pipelines for processing structured, unstructured, and vector-embedded data used in machine learning tasks.
- Configure and fine-tune SQL, NoSQL, and vector databases to support fast retrieval and low-latency inference in AI systems.
- Use AWS, Kubernetes, and infrastructure-as-code tools like Terraform or CloudFormation to develop resilient, production-grade AI infrastructure.
- Identify performance bottlenecks, improve efficiency in speed and cost, and ensure AI workflows remain stable and fault-tolerant.
- Collaborate with machine learning researchers, platform engineers, and domain scientists to transition experimental models into scalable production systems.
