AI/ML Engineer

AI/ML Engineer

Pay Range: $52hr - $57hr

Requirement/Must Have:

• 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 Optimization - 2–3+ years.
• AWS SageMaker (Training, Deployment, Endpoints) - 2–3 years (Hands-on).
• Data Preprocessing & Feature Engineering - 3+ years.
• Model Evaluation Techniques - 3+ years.
• Version Control (Git) - 2+ years.
• ML Lifecycle / Model Management - 2+ years.

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.

Nice to Have:

• Experience with TensorFlow / PyTorch - 1–2+ years.
• Experience with MLOps tools & practices - 1–2 years.
• Knowledge of AWS ecosystem (beyond SageMaker) - Working Knowledge.
• Familiarity with CI/CD for ML workflows - Basic–Intermediate.
• Domain experience in healthcare, finance, or retail (optional).

Skills:

• 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.
• 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.