AI/ML Engineer

Job Description

Job Description

AI/ML Engineer
Location: WOODLAND HILLS, CA

Skill Area Required Experience]
Overall AI/ML Engineering Experience - 5 Years
Machine Learning Algorithms (Classification & Regression) - 4 Years
AutoML Tools (SageMaker Autopilot / H2O / AutoKeras)2–3 Years
Python Programming - 4 Years
ML Libraries (Scikit-learn, Pandas, NumPy)3–4 Years
Model Fine-Tuning & Hyperparameter Optimization2–3 Years
AWS SageMaker (Training, Deployment, Endpoints)2–3 Years (Hands-on)
Data Preprocessing & Feature Engineering3 Years
Model Evaluation Techniques3 Years
Version Control (Git)2 Years
ML Lifecycle / Model Management2 Years

Skill Area
Expected ExposureTensorFlow / PyTorch1–2 Years
MLOps Tools & Practices1–2 Years
AWS Ecosystem (beyond SageMaker) Working Knowledge
CI/CD for ML WorkflowsBasic–Intermediate
Deep Learning Models

Job Title: AI/ML Engineer Experience: 5 Years Employment Type: Contract Job Summary We are seeking a skilled AI/ML Engineer with strong experience in building and deploying machine learning models, including classification and regression models, leveraging AutoML techniques, and performing model fine-tuning. The ideal candidate will have hands-on expertise in Python-based ML libraries and experience working with AWS SageMaker for scalable model development and deployment. Key Responsibilities Design, develop, and deploy machine learning models for business use cases Work on classification and regression problems with structured and unstructured data Utilize AutoML frameworks to accelerate model building and optimization Perform model fine-tuning, hyperparameter optimization, and performance enhancement Develop and implement data pipelines for model training and evaluation Collaborate with cross-functional teams to translate business problems into ML solutions Deploy and manage ML models using AWS SageMaker Monitor model performance and ensure continuous improvement Ensure best practices in data handling, model versioning, and reproducibility Required Skills & Qualifications Strong experience in Machine Learning algorithms (Classification & Regression) Hands-on experience with AutoML tools (e.g., SageMaker Autopilot, H2O, AutoKeras, etc.) Proficiency in Python and major ML libraries: Scikit-learn Pandas, NumPy TensorFlow / PyTorch (preferred) Experience in model fine-tuning and hyperparameter optimization Strong knowledge of AWS SageMaker (training, deployment, endpoints) Good understanding of data preprocessing, feature engineering, and model evaluation Experience with version control (Git) and ML lifecycle management Preferred Skills Experience with MLOps practices and tools Knowledge of cloud services (AWS ecosystem) beyond SageMaker Exposure to deep learning models and frameworks Familiarity with CI/CD pipelines for ML workflows Domain experience in healthcare, finance, or retail (optional)