Note: The job is a remote job and is open to candidates in USA. HCL Global Systems Inc is seeking a skilled AI/ML Engineer with strong experience in building and deploying machine learning models. 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.
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
Skills
- 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
- 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
- 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
- TensorFlow / PyTorch 1–2+ Years
- MLOps Tools & Practices 1–2+ Years
- AWS Ecosystem (beyond SageMaker) Working Knowledge
- CI/CD for ML Workflows Basic–Intermediate
- Deep Learning Models
- 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
Company Overview