Offer summary

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field., 2+ years of experience in developing and deploying AI/ML models., Proficient in programming languages such as Python, Java, or C++., Strong understanding of machine learning algorithms and frameworks like TensorFlow and PyTorch..

Key responsibilities:

Develop and implement AI and machine learning models for various applications.

Collaborate with cross-functional teams to gather requirements and translate them into technical solutions.

Optimize and scale ML models for performance and efficiency in production environments.

Document processes, experiments, and code in a clear and maintainable manner.

Job description

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Key Responsibilities:

Develop and implement AI and machine learning models for various applications (e.g., NLP, computer vision, recommendation systems).
Collaborate with cross-functional teams to gather requirements and translate them into technical solutions.
Optimize and scale ML models for performance and efficiency in production environments.
Deploy models using frameworks such as TensorFlow, PyTorch, or ONNX, and tools like Docker and Kubernetes.
Build and maintain data pipelines and infrastructure to support AI solutions.
Conduct research to stay updated on the latest trends and advancements in AI/ML.
Perform model evaluation, A/B testing, and fine-tuning to improve accuracy and efficiency.
Document processes, experiments, and code in a clear and maintainable manner.

Required Qualifications:

Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or related field.
2+ years of experience in developing and deploying AI/ML models.
Proficient in programming languages such as Python, Java, or C++.
Strong understanding of machine learning algorithms, deep learning architectures, and statistical modeling.
Experience with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).

Preferred Qualifications:

Experience with MLOps practices and tools (e.g., MLflow, Kubeflow).
Understanding of data engineering and working with large-scale datasets.
Knowledge of real-time ML systems and streaming data frameworks (e.g., Kafka, Spark).
Contributions to open-source AI projects or research publications.

Required profile