DE&A - AIML - Deep Learning - Generative AI

• 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.)

• 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)

• 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

• 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

• 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