- Strong software engineering background and experience in production-grade AI delivery systems
Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
- Proficient in at least one skill in C++/CUDA, Python/PySpark, Java/Scala
- Good experience in cloud-native AI tools (Azure, AWS, GCP), Agentic/DL/LLM/ML frameworks (React, LangChain, LangGraph, TensorFlow, PyTorch, OpenCV, Hugging Face), and AIOps platforms
- Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
Proficiency in model evaluation, distributed training, and hyperparameter optimization
Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
- Proficiency in model evaluation, distributed training, and hyperparameter optimization
- Get familiar with AI/ML lifecycle, model architectures (including deep reinforcement learning, LLMs, RAG, vector search, MoE, foundation models), and structured/unstructured data pipelines
- Effective communicator who can explain complex technical ideas to technical and business audiences
Ability to work independently in fast-paced, cross-functional environments
- Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, or a related technical field; PhD preferred
- Academic or applied focus on AI, deep learning, or intelligent systems is preferred