About the role
Requirements:
- Bachelor's or master's degree in computer science, applied mathematics, or a related technical field; PhD preferred.
- 4-7 years of hands-on experience building and deploying production-grade AI/ML systems, including LLMs or GenAI pipelines.
- Strong software engineering background and experience working in production-grade AI/ML environments.
- Deep Expertise in C++/CUDA, Python/PySpark, Java/Scala, cloud-native AI/LLM/ML tools (Azure, AWS, GCP), DL/LLM/ML frameworks (LangChain, TensorFlow, PyTorch, OpenCV, Hugging Face), and AIOps platforms.
- Deep Understanding of AI/ML algorithms, including supervised/unsupervised learning, time series, neural networks, transformers, Bayesian inference, reinforcement learning, and BERT/CLIP.
- Strong understanding of deep learning architectures (e. g., CNNs, RNNs, transformers, and GANs).
- Experience in building distributed, real-time inference systems and optimizing inference at scale.
- Proficiency in model evaluation, distributed training, and hyperparameter optimization.
- Strong communication, mentoring, and architectural documentation skills.
- Ability to work independently in fast-paced, cross-functional environments.