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Python Data Engineer III- Machine Learning

JPMorgan Chase & Co.
Glasgow
5 months ago
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Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.


As a Data Engineer III at JPMorgan Chase within the Developer Platforms and Insights team, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Design, develop, and deploy machine learning models to solve complex business problems. Collaborate with cross-functional teams to integrate ML models into production systems. Utilize PyTorch, Scikit-learn, NumPy, and Pandas for data analysis and model development. Develop and maintain APIs for model deployment and integration. Fine-tune large language models to enhance performance and accuracy. Apply deep learning architectures such as LSTMs and Transformers to relevant projects. Stay updated with the latest advancements in generative AI and implement innovative solutions. Conduct statistical analysis to support model development and validation.

Required qualifications, capabilities, and skills

Formal training or certification on Data engineering concepts and applied experience cProven experience in building and deploying machine learning models. Hands-on experience with PyTorch, Scikit-learn, NumPy, and Pandas. Proficient in Python programming language and building APIs. Solid understanding of statistics and machine learning theory. Experience with deep learning architectures, including LSTMs and Transformers. Experience in fine-tuning large language models. Knowledge of generative AI (GenAI) technologies. Strong problem-solving skills and the ability to work independently and collaboratively. Excellent communication skills to convey complex technical concepts to non-technical stakeholders.

Preferred qualifications, capabilities, and skillsExperience with cloud platforms such as AWS, Google Cloud, or Azure. Familiarity with version control systems like Git. Experience in deploying models using containerization technologies like Docker.

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