Machine Learning (ML) Engineer

• Proficiency in Python or R for machine learning model development.
• Experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.
• Knowledge of cloud platforms (AWS, Azure, GCP) for deploying ML models.
• Experience with data processing libraries such as Pandas, NumPy, and SQL.
• Strong understanding of machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
• Knowledge of NLP, computer vision, or time-series analysis is a plus.
• Familiarity with version control systems like Git.
• Experience with MLOps tools such as MLflow, Kubeflow, or Sagemaker.
• Strong problem-solving and analytical skills.
• Excellent communication and teamwork abilities.
• Ability to explain complex technical concepts to non-technical stakeholders.
• Strong attention to detail and a commitment to producing high-quality work.

• Design, develop, and deploy machine learning models and algorithms to address business needs.
• Build scalable data pipelines to support machine learning workflows.
• Collaborate with data scientists to transform prototypes into production-ready solutions.
• Optimize and fine-tune models to improve accuracy, speed, and scalability.
• Develop APIs to integrate ML models into production applications.
• Monitor and maintain deployed models, ensuring performance and accuracy over time.
• Implement best practices for data preprocessing, feature engineering, and model evaluation.
• Work on advanced ML techniques such as NLP, computer vision, and deep learning as needed.
• Collaborate with cross-functional teams to understand business requirements and translate them into ML solutions.
• Ensure compliance with data governance, security, and privacy standards.
• Keep up-to-date with the latest advancements in AI/ML technologies and tools.

Experience in developing and maintaining ML pipelines in production. Familiarity with DevOps practices and tools (CI/CD). Knowledge of data governance, security, and compliance in ML systems. Experience working with large-scale datasets and distributed systems (e.g., Spark, Hadoop).