DE&A - AIML - Auto ML

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