In this role, you'll lead the design and delivery of production-grade ML and generative AI systems that solve complex product and business problems at scale. You'll own architecture and implementation across data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation, and observability. You’ll drive advanced use cases across deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and LLM applications, including RAG, tool use, evaluation harnesses, and safety controls.
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Responsibilities
- Shape performance, reliability, latency, and cost efficiency in live environments.
- Guide technical direction on topics such as model selection, distributed training and inference, GPU utilization, model compression, prompt and retrieval optimization, drift detection, retraining strategy, and responsible AI controls.
- Mentor other engineers and turn best practices into reusable patterns and platform capabilities.
Requirements
- 7-9+ years of experience in software engineering and machine learning, with a track record of leading and delivering complex, production-scale AI systems.
- Expert-level programming skills in Python, along with strong software engineering expertise in languages such as Java or Go.
- Deep knowledge of machine learning, deep learning, and optimization techniques across structured data, NLP, search, ranking, and recommendation problems.
- Extensive experience designing and operating end-to-end ML systems, from data ingestion and experimentation to deployment, observability, and lifecycle management.
- Strong hands-on experience with modern ML and LLM tooling (e.g., PyTorch, TensorFlow, scikit-learn), including fine-tuning, evaluation, orchestration, and model serving.
- Deep expertise in MLOps and platform engineering, including model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, and automated validation.
- Strong architectural understanding of distributed systems, event-driven services, streaming data, and cloud-native ML platforms.
- Define robust evaluation and governance strategies, including offline benchmarking, online experimentation, hallucination analysis, model risk assessment, and responsible AI practices.
Work Conditions
- Expected Travel: 0 - 10%
- Employment Type: Regular Full Time
- Career Status: Professional
- Additional Locations: #LI-Hybrid
Location: Riyadh, SA
