About the Role:
We are looking for a talented and motivated Machine Learning Engineer to join our team. As a Machine Learning Engineer, you will be responsible for building and deploying machine learning models that solve real-world problems. This role is perfect for individuals passionate about working with data, algorithms, and large-scale machine learning systems. The position offers a competitive salary and growth opportunities, and is open to candidates with 0 to 5 years of experience.
Key Responsibilities:
- Design, develop, and deploy machine learning models and algorithms to solve business problems.
- Work with cross-functional teams to understand data requirements and business needs.
- Build data pipelines and architectures for large-scale machine learning projects.
- Experiment with various machine learning techniques such as supervised and unsupervised learning, deep learning, reinforcement learning, etc.
- Evaluate and fine-tune models for performance and scalability.
- Work with cloud platforms (AWS, Azure, or Google Cloud) to deploy machine learning solutions.
- Collaborate with data engineers and software developers to integrate ML models into production systems.
- Keep up-to-date with the latest trends and advancements in machine learning and AI.
Required Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or related fields.
- Strong programming skills in Python, R*, or Java*.
- Familiarity with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.
- Understanding of algorithms, data structures, and optimization techniques.
- Knowledge of supervised, unsupervised, and deep learning techniques.
- Strong analytical skills and experience with data cleaning, preprocessing, and feature engineering.
- Familiarity with cloud services like AWS, Azure, or Google Cloud for model deployment.
Preferred Qualifications:
- Master’s degree in Computer Science, Machine Learning, Data Science, or related fields.
- Experience working with large datasets and big data tools like Hadoop, Spark, or Kafka.
- Hands-on experience in Natural Language Processing (NLP), computer vision, or reinforcement learning.
- Familiarity with model optimization and hyperparameter tuning techniques.
- Knowledge of containerization and orchestration tools such as Docker and Kubernetes.
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