Lead Machine Learning Engineer
1 week ago
About the Role:
We are seeking an experienced Machine Learning Engineer Manager with 8-10 years of hands-on experience in machine learning, data science, and AI development, combined with proven leadership skills. You will lead a team of talented ML engineers and data scientists to design, build, and deploy scalable machine learning solutions that drive business impact. This role involves strategic planning, cross-functional collaboration, and mentoring team members to excel technically and professionally.
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.
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