MANEVA CONSULTING
Machine Learning Engineer
- Compensation
- Not disclosed
- Location
- Bangalore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Education :
- - Master's degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field.
- Experience :
- - 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role.
- Machine Learning Expertise :
- - Solid theoretical and practical understanding of machine learning algorithms and techniques.
- - Hands-on experience with ML frameworks such as PyTorch and Scikit-learn.
- - Strong understanding of feature engineering, model evaluation metrics, and hyperparameter tuning.
- - Experience developing and optimizing machine learning models for real-world applications.
- Data & Technical Skills :
- - Experience working with SQL and NoSQL databases.
- - Understanding of data warehousing concepts and data management practices.
- - Experience handling and processing large-scale datasets.
- - Strong analytical and problem-solving capabilities with a pragmatic approach to solution development.
- Communication & Collaboration :
- - Strong verbal and written communication skills.
- - Ability to explain complex machine learning concepts and technical solutions to both technical and non-technical audiences.
- - Ability to work effectively in a collaborative, cross-functional environment.
Nice to have
- - Experience working with large-scale ML/data-processing environments.
- - Exposure to modern data engineering and ML engineering practices.
- - Strong portfolio of machine learning projects, research, or relevant professional implementations.
Full description
Role Overview :
We are looking for a skilled Machine Learning Engineer with 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role. The ideal candidate should have a strong foundation in machine learning algorithms, hands-on experience with ML frameworks, and the ability to work with large-scale datasets.
The candidate will be responsible for developing, evaluating, and improving machine learning solutions while collaborating with cross-functional teams to translate business and technical requirements into effective ML solutions.
Key Responsibilities :
- Design, develop, and implement machine learning models and algorithms to solve complex business and technical problems.
- Apply strong knowledge of machine learning concepts to perform model development, evaluation, optimization, and continuous improvement.
- Use ML frameworks such as PyTorch and Scikit-learn to build and experiment with machine learning solutions.
- Perform feature engineering, data preprocessing, model evaluation, and hyperparameter tuning to improve model performance.
- Work with SQL and NoSQL databases to extract, transform, analyze, and manage data required for ML workflows.
- Process and analyze large datasets using appropriate data-processing and data-warehousing techniques.
- Collaborate with data scientists, software engineers, product teams, and business stakeholders to understand requirements and deliver practical ML solutions.
- Troubleshoot model and data-related issues and apply strong analytical and problem-solving skills to develop effective solutions.
- Communicate complex machine learning concepts, methodologies, and results clearly to both technical and non-technical stakeholders.
Required Qualifications :Education :
- Master's degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field.
Experience :
- 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role.
Machine Learning Expertise :
- Solid theoretical and practical understanding of machine learning algorithms and techniques.
- Hands-on experience with ML frameworks such as PyTorch and Scikit-learn.
- Strong understanding of feature engineering, model evaluation metrics, and hyperparameter tuning.
- Experience developing and optimizing machine learning models for real-world applications.