Gen AI - Data Scientist

Location: Dhahran

Experience range : 5+

JD :

Gen AI - Data Scientist :

• Develop and deploy machine learning and deep learning models for time series forecasting, NLP, and sensor/IoT data analysis.
• Fine-tune and deploy state-of-the-art (SOTA) models, including LLMs, using frameworks like HuggingFace, and methods like LoRA, Q-LoRA, and full model fine-tuning.
• Implement and evaluate bagging and boosting models (e.g., XGBoost, LightGBM, CatBoost) for structured and sensor data.
• Design and optimize neural network architectures, including CNNs, RNNs, Transformers, and attention-based models using PyTorch.
• Build and evaluate model performance using appropriate metrics and validation strategies.
• Work with smart sensor data and IoT systems, extracting features and building predictive models for anomaly detection, failure prediction, and optimization.
• Develop and expose model inference as REST APIs using FastAPI or similar web frameworks.
• Collaborate with DevOps and engineering teams to operationalize models using Docker, CI/CD pipelines, and cloud platforms.
• Maintain and improve data pipelines, feature engineering, and model monitoring in production environments.

Gen AI Engineer :

• Strong foundation in deep-learning mathematics (linear algebra, probability, optimization
• Expert in PyTorch and/or TensorFlow.
• Mastery of Hugging Face Transformers/Diffusers and ability to write custom CUDA kernels
• Experience with distributed training on GPU/TPU clusters.
• Proficiency with prompt-engineering tools (LangChain, LlamaIndex).
• RAG expertise: building vector stores, hybrid retrieval, and multi-agent orchestration.
• MCP server knowledge: designing, deploying, and scaling services that manage agent communication and state (REST/gRPC, message queues, load balancing).
• MLOps stack: Docker, Kubernetes, Helm, CI/CD, Triton/TensorRT-Inference Server, Prometheus, Grafana.
• Large-scale data pipelines (Spark, Beam, Dask) and data versioning (LakeFS, DVC, Weights & Biases).
• Publications or patents in generative AI (NeurIPS, ICML, ICLR, CVPR).
• Open-source contributions to major libraries (Hugging Face, PyTorch, Diffusers).
• Domain expertise in finance, healthcare, gaming, or creative media.