Job Details

Overview

. The Lead ML Engineer is expected to drive the end-to-end machine learning process, from data collection and preprocessing to model deployment and maintenance. The role involves collaborating with cross-functional teams, mentoring junior engineers, and ensuring the scalability and performance of ML solutions.

Job Description

The Lead Machine Learning Engineer is responsible for overseeing and guiding the development, implementation, and optimization of machine learning models and algorithms within an organization. This role combines hands-on technical work with leadership, as the engineer leads a team in creating innovative solutions that leverage machine learning to solve business problems

Education

: Bachelors degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. A Masters or Ph.D. in a related field is preferred. Relevant certifications in machine learning, AI, or data science (e.g., Google Cloud AI, AWS Machine Learning) are a plus.

Roles and Responsibilities

1. Lead the design
2. development
3. and deployment of machine learning models
4. Build and maintain data pipelines for efficient data collection
5. processing
6. and analysis
7. Develop and implement algorithms for predictive modeling
8. Collaborate with cross-functional teams to understand business problems and translate them into machine learning solutions
9. Optimize and fine-tune machine learning models for scalability
10. performance
11. and accuracy
12. Mentor and guide junior machine learning engineers
13. Conduct research on the latest ML techniques and technologies
14. Evaluate and select appropriate machine learning algorithms for different use cases
15. Perform feature engineering and selection to improve model performance
16. Ensure proper data preprocessing and cleaning to enhance model results
17. Monitor and maintain deployed models
18. ensuring they continue to deliver value
19. Implement and maintain automated systems for model training and deployment
20. Create and maintain documentation for models and ML processes
21. Stay up to date with industry trends in machine learning and artificial intelligence
22. Communicate technical findings and progress to both technical and non-technical stakeholders.

Required Skills

1. Machine learning
2. Deep learning
3. Python
4. TensorFlow
5. PyTorch
6. Scikit-learn
7. Data preprocessing
8. Model training and evaluation
9. Model deployment
10. Algorithm development
11. Neural networks
12. Natural language processing (NLP)
13. Reinforcement learning
14. Statistical analysis
15. Data analysis
16. SQL
17. Cloud platforms (AWS
18. Azure
19. Google Cloud)
20. Big data technologies (Hadoop
21. Spark)
22. Data pipelines
23. Feature engineering
24. Hyperparameter tuning
25. Version control (Git)
26. Problem-solving
27. Team leadership
28. Communication skills
29. Agile methodologies
30. Research and development
31. ML frameworks.