Experience:
Proven experience in developing machine learning models and systems.
Proficiency in Python and its libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
Strong programming experience with Rust.
Hands-on experience with data analysis, statistical modeling, and data preprocessing.

Skills:
Expertise in data science and machine learning algorithms (e.g., supervised/unsupervised learning, deep learning, reinforcement learning).
Solid understanding of distributed computing and data processing frameworks (e.g., Spark, Hadoop).
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes).
Knowledge of model evaluation, tuning, and deployment practices.
Experience with version control systems (e.g., Git) and CI/CD pipelines.

Preferred Qualifications
Experience with reinforcement learning and advanced optimization techniques.
Familiarity with Rust-based data processing libraries and frameworks.
Understanding of scalable system architecture and performance tuning.
Strong mathematical background in linear algebra, probability, and optimization.
Knowledge of MLOps practices for maintaining and scaling production systems.
Prior experience in high-stakes domains such as finance, healthcare, or autonomous systems.