Description
Strong software engineering background and experience in production-grade AI delivery systems
Solid 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 quick-paced, cross-functional settings

Qualifications
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

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