- Strong software engineering background and experience in production-grade AI delivery systems
- 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.)
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Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
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Proficiency in model evaluation, distributed training, and hyperparameter optimization
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Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
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Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
- 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
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Ability to work independently in fast-paced, cross-functional environments
Preferred Skills
- Experience in regulated industries (e.g., finance, healthcare, insurance)
- Excellent communication and stakeholder engagement skills
- Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs)
- Solid in AI/ML algorithms including Neural Network, Transformers, Diffusions, Generative Modeling, Bayesian Inference, Reinforcement Learning, BERT/CLIP
- Proficient in API, MCP and Microservices technologies
- Track records in large-scale, real-time AI/GenAI/AgenticAI/ML database and solution technologies
- Background in responsible AI/ML, model interpretability, and fairness auditing