Opening Summary

We are seeking a highly skilled and motivated Machine Learning (ML) Engineer to join our dynamic team. The ML Engineer will design, build, and deploy machine learning models and systems to solve real-world business problems. You will work closely with data scientists, product managers, and software engineers to develop scalable and efficient ML solutions, ensuring the models deliver value while maintaining performance, scalability, and interpretability.

Requirements

• 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.

Responsibilities

• 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.

Preferred Qualification

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).