Description

You will define and execute enterprise-scale machine learning solutions.

Responsibilities

• Define ML architecture, best practices, and performance standards for enterprise-scale solutions.
• Lead the full lifecycle of models from data preprocessing through deployment and monitoring.
• Build scalable ML pipelines and APIs using Python and Golang for backend services.
• Design and manage CI/CD pipelines for ML, including automated retraining and versioning.
• Apply hypothesis testing, Bayesian methods, and model interpretability to ensure reliable insights.

Required Skills

• 8+ years of experience in applied ML or data science, including 3+ years at a senior or staff level.
• Expert proficiency in Python for ML development.
• Proven experience deploying traditional ML models to production with measurable business impact.
• Strong knowledge of ML frameworks including Scikit-learn, XGBoost, and LightGBM.
• Hands-on MLOps experience with Kubeflow, Databricks, or AWS SageMaker Pipelines.
• Strong database skills in SQL and NoSQL.
• Cloud deployment expertise across AWS, Azure, or GCP.
• Experience with model monitoring, drift detection, and automated retraining.
• Familiarity with distributed computing frameworks like Spark or Ray.

Key Skills

Education

Any Graduate