Description
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.)
Robust 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)
Solid 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
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
Robust 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

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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