AI ENGINEER

Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related fields (PhD is a plus). Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. Hands-on experience with: LLMs / Transformers (HuggingFace, OpenAI, Mistral, LLaMA, Qwen, etc.) Training, fine-tuning, or distillation of medium-to-large-scale models Prompt engineering and retrieval-augmented generation (RAG) Reinforcement learning or sequential decision-making models Computer vision or multimodal architectures (preferred, not mandatory) Solid understanding of ML fundamentals: supervised/unsupervised learning, optimization, evaluation metrics. Experience with vector databases, embeddings, and text-processing pipelines. Ability to design reproducible experiments and interpret research papers. Familiarity with Docker, Linux, and cloud platforms (Azure, AWS, or GCP). Experience deploying models through APIs, microservices, or GPU inference servers. Strong English skills with the ability to comprehend technical documents, write clear reports, and communicate effectively in a professional setting. Nice-to-Have Experience with distributed training frameworks (DeepSpeed, PyTorch Lightning, Ray, Colossal-AI). Knowledge of CUDA, GPU profiling, or model optimization/quantization (GGUF, TensorRT, ONNX). Experience building agentic workflows, autonomous AI agents, or tools-based LLM systems. Background in DevOps/MLOps: CI/CD for ML, model monitoring, A/B testing. Contributions to open-source AI projects or research publications.