Lead the entire ML lifecycle from data collection and analysis to model deployment, monitoring, and optimization.
Apply deep learning and NLP techniques to develop solutions, potentially enhancing systems like search or recommendation engines.
Design and implement end-to-end ML pipelines, incorporating MLOps best practices for CI/CD, containerization (Docker, Kubernetes), and cloud deployment (AWS, GCP, Azure).
Utilize LLM knowledge, including prompt engineering and fine-tuning, to build advanced generative AI applications and conversational AI solutions.
Perform comprehensive data analytics, including statistical analysis and feature engineering, to inform model development and extract actionable insights from large datasets.
Write production-quality, robust code in Python (and potentially other languages like Java or Scala), ensuring code quality through reviews and testing.
Collaborate with cross-functional teams, including data scientists, data engineers, and product managers, to translate business requirements into technical ML solutions.
Requirements
Required Skills and Qualifications
Proven experience as a Machine Learning Engineer with a strong portfolio of deployed production models.
Proficiency in Python and relevant ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Expertise in data science methodologies, statistical analysis, and data analytics.
Hands-on experience with MLOps tools and practices for managing the ML application lifecycle.
Strong understanding of NLP and experience with LLMs and prompt engineering techniques.
Solid software engineering background with knowledge of data structures, algorithms, and system design.
Excellent problem-solving, communication, and collaboration skills.
Saudi Arabia