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Job Responsibilities:
- Collaborate with cross-functional teams to understand requirements and translate them into technical solutions.
- Design and develop data pipelines to preprocess and transform data for AI/ML models.
- Train and evaluate AI/ML models using large datasets.
- Optimize and fine-tune AI/ML models for performance and accuracy.
- Deploy AI/ML models into production environments.
- Monitor and maintain deployed models, ensuring their performance and reliability.
- Stay up-to-date with the latest advancements in AI/ML technologies and techniques.
- Design graph data models specifically for algorithm optimization.
- Design and add the data from the physical and logical infrastructure components and their relationships.
- Developing and implementing data ETL pipelines within AWS
Required qualifications, capabilities, and skills:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- At least 5 years of demonstrated experience in applied AI/ML engineering.
- Strong programming skills in Python, with experience in developing and maintaining production-level code.
- Experience with designing and implementing graph databases, such as Amazon Neptune, TigerGraph.
- Proficiency in working with large datasets and data preprocessing.
- Solid understanding of AI/ML algorithms and techniques, including deep learning, reinforcement learning, and natural language processing.
- Familiarity with AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, and Keras.
- Experience in creating infrastructure graph data models
- Experience with cloud platforms, such as AWS or Azure, for deploying and scaling AI/ML models.
- Experience with ETL tools such as Airflow, and Jenkins.
Preferred Qualifications, capabilities and skills
- Experience with distributed computing frameworks, such as Apache Spark.
- Knowledge of graph-based AI/ML algorithms and techniques.
- Familiarity with DevOps practices for AI/ML model deployment and monitoring.
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