Senior Machine Learning Engineer

About the Role:

We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker .

Key Responsibilities:

• Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
• Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
• Apply a wide range of statistical, machine learning, and deep learning techniques , including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
• Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
• Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
• Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
• Monitor and improve model performance, scalability, and reliability in production.
• Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).

Required Qualifications:

• 7+ years of experience in machine learning, data science, or related fields.
• Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Hands-on experience with PySpark for big data processing and model development.
• Proficient in building models on large-scale datasets (terabytes to petabytes).
• Solid understanding of statistical analysis , probability, hypothesis testing, and experimental design.
• Experience with Amazon SageMaker (or similar cloud-based ML platforms).
• Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
• Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
• Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.

Preferred Qualifications:

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
• Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
• Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.