JPMorgan Applied AI/ML Lead 

United States, New Jersey, Jersey City 

792744046

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.