AI/ML Engineer (US Citizen)

Role: AI/ML Engineer with Deep Learning Experience, someone who has worked on Keras Location: San Jose, CA Visa - US Citizen Required Skill Sets on top of the above skills: • Experience in Data Science and DeepLearning frameworks. • Customer requirement analysis, cross team collaboration • Software Development Lifecycle, strong Software Design/Development experience • Computer Science or Computer Engineering or equivalent technical degree • must be able to recognize potential issues, and compose technical communications in GitHub) • Experience working with Windows, MacOS, and Ubuntu environments • Excellent written and oral communication skills • Being a team player with a positive attitude and people skills • Open to learning new internal technical tools Required Python Skills • Python installation, environment setup and Jupyter Notebook • Object and Data Structures basics • Comparison Operators and Statements • Methods and Functions • Errors and Exception handling • Built-in functions and Python Generators • Using scientific Python libraries numpy, pandas, matplotlib, scikit-learn • Use data visualization with Python Machine Learning Prerequisites • Overview of ML explaining life cycle like Data Acquisition->Cleaning->Training a model->Testing a model->Evaluating a model • Knowledge on deploying models on mobile devices iOS/Android • Knowledge on C++ for custom functions and writing unit test cases. • Strong debugging skills on C++/Python code. • Basic jargons of ML which include Cost functions, Gradient Descent, Back Propagation, Activation functions etc • Supervised, Unsupervised, Reinforcement learning • Classifications and Regression • Using Datasets • Types of algorithms like Decision Tree, K means etc • Using scientific Python libraries numpy, pandas, matplotlib, scikit-learn • Importing data in python, clean, preprocess data and manipulate data frames with pandas • Neural networks, CNN, RNN/LSTM Keras 3 Prerequisites • Multi-Backend Installation: Installing Keras 3 and configuring backends (JAX, PyTorch, or TensorFlow) using the KERAS\_BACKEND environment variable. • Core Data Structures: Understanding Layers, Models, and the fundamental difference between the Sequential API, Functional API, and Model Subclassing. • Backend-Agnostic Ops: Familiarity with the keras.ops namespace (the cross-framework NumPy-like API) and keras.random for writing framework-independent code. • State Management: Concepts of statelessness vs. statefulness, especially when working with the JAX backend and Keras 3’s functional layer calls. • Training & Evaluation: Mastering the high-level .fit(), .evaluate(), and .predict() workflows, as well as writing Custom Training Loops using GradientTape (TF/PyTorch) or jax.grad. • The Distribution API: Knowledge of keras.distribution for multi-GPU and TPU training (Data Parallelism and Model Parallelism). • Optimization & Compilation: Understanding XLA (Accelerated Linear Algebra) and how to leverage jit\_compile for performance across different hardware. • Serialization: Using the modern .keras v3 format for saving/loading models across different frameworks and platforms.