As a Machine Learning Engineer, you will play a pivotal role in developing and deploying advanced ML models to drive innovation and efficiency across our organization. This role requires a deep understanding of machine learning algorithms, data processing techniques, and the ability to work collaboratively with cross-functional teams. The ideal candidate will be passionate about leveraging data to solve complex problems and will have a track record of successfully implementing ML solutions in energetic environments.
Experience Required:
3+ years
Key Responsibilities:
- Design, develop, and implement machine learning algorithms to solve complex business problems.
- Collaborate with data scientists and software engineers to integrate ML models into production systems.
- Conduct thorough testing and validation of ML models to ensure accuracy and performance.
- Optimize machine learning models for speed, scalability, and resource efficiency.
- Provide technical leadership and mentorship to junior ML team members.
- Stay updated with the latest trends and advancements in machine learning and artificial intelligence.
- Participate in the full software development lifecycle, from requirements gathering to deployment and maintenance.
- Work closely with stakeholders to understand and define project requirements and deliver ML solutions.
- Develop and maintain comprehensive documentation for ML models and related processes.
- Contribute to the design and implementation of data pipelines to support ML workflows.
Qualification Criteria:
- A minimum of 3 years of experience in machine learning or a related field.
- Strong proficiency in programming languages such as Python, R, or Java.
- Experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
- Knowledge of data preprocessing techniques and experience with large datasets.
- Ability to work in an agile development environment.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders.
- Familiarity with cloud platforms like AWS, Azure, or Google Cloud.
- Understanding of statistical methods and mathematical concepts relevant to machine learning.
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
Primary Skills:
- Python
- Machine Learning
- Data Science
Key Skills:
- TensorFlow
- PyTorch
- Scikit-learn
- SQL
- AWS
- Docker
- Git
- APIs
- NLP
- Data Visualization
- Feature Engineering
- Hyperparameter Tuning
- Cloud Computing
- Big Data
- Statistical Modeling
- Data Wrangling
- Reinforcement Learning
- Deep Learning
- Natural Language Processing
- Data Mining