Lead Machine Learning Engineer

About the Role:

Machine Learning Engineer Manager

Key Responsibilities:

• Design, develop, and deploy machine learning models and algorithms for various applications.
• Collaborate with data scientists, data engineers, and product teams to understand requirements and deliver robust ML solutions.
• Build scalable data pipelines and feature engineering workflows for model training and inference.
• Optimize ML models for performance, accuracy, and scalability in production environments.
• Perform data analysis, exploratory data mining, and feature extraction to improve model quality.
• Research and implement state-of-the-art machine learning techniques and tools to solve business challenges.
• Write clean, maintainable, and efficient code following best software engineering practices.
• Monitor and maintain deployed ML systems, troubleshooting and resolving issues as they arise.
• Mentor junior engineers and contribute to team knowledge sharing and best practices.

Qualifications:

• Bachelors or master’s degree in computer science, Engineering, Statistics, Mathematics, or related field; PhD is a plus.
• 8-10 years of professional experience in machine learning engineering, data science, or related fields.
• Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of classical ML algorithms (regression, classification, clustering), as well as deep learning architectures (CNNs, RNNs, Transformers, etc.).
• Experience with cloud platforms and ML infrastructure (AWS, GCP, Azure, Kubernetes, Docker).
• Familiarity with data processing frameworks (Spark, Hadoop) and database technologies.
• Strong knowledge of data structures, algorithms, and software engineering best practices.
• Excellent problem-solving skills and ability to work independently and collaboratively.
• Effective communication skills to present technical concepts to diverse audiences.

Preferred Skills:

• Experience with NLP, Computer Vision, or time-series analysis.
• Familiarity with MLOps, model deployment, and monitoring tools and cloud platforms like GCP/AWS/Azure
• Experience with big data technologies and streaming platforms (Kafka, Flink).
• Knowledge of Agile methodologies and CI/CD pipelines.