DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Lead Software Engineer at JPMorganChase within the Data Products Team youare an integral part of an agile team that works to enhance build and deliver trusted market-leading technology products in a secure stable and scalable way. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
- Collaborate with data scientists to facilitate training fine-tuning and deployment of ML models including foundational and generative models.
- Integrate trained models into production applications (e.g. anomaly detection automated reporting agentic AI workflows).
- Develop APIs microservices and user interfaces to expose model capabilities to business users and other systems.
- Design and implement prompt engineering strategies and agentic architectures for autonomous AI workflows.
- Monitor troubleshoot and optimize model performance scalability and reliability in production environments.
- Act as a technical liaison between data science engineering and product teams to ensure seamless integration and delivery.
- Document processes workflows and best practices for model deployment and application integration.
Required qualifications capabilities and skills
- Formal training or certification on software engineering concepts and 5 years applied experience
- Proficiency in Python and experience building APIs/microservices.
- Experience with ML frameworks (e.g. PyTorch TensorFlow Hugging Face) and foundational models (LLMs generative AI).
- Familiarity with prompt engineering and agentic workflows.
- Strong understanding of cloud platforms (AWS GCP Azure) and MLOps practices.
- Excellent communication and collaboration skills.
Preferred qualifications capabilities and skills
- Experience with anomaly detection automated reporting or narrative generation systems.
- Exposure to vector databases retrieval-augmented generation (RAG) or semantic search.
- Experience with containerization (Docker Kubernetes) and CI/CD pipelines.
- Knowledge of security and compliance in AI/ML deployments.
- Experience with Databricks ML Ops.
- Familiarity with regression/classification models and their integration into production systems.
DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.As a Lead Software Engineer at JPMorganChase within the Data Products Team youare an integral part of an agile team that works to enhance build and deliver trusted market-...
DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Lead Software Engineer at JPMorganChase within the Data Products Team youare an integral part of an agile team that works to enhance build and deliver trusted market-leading technology products in a secure stable and scalable way. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
- Collaborate with data scientists to facilitate training fine-tuning and deployment of ML models including foundational and generative models.
- Integrate trained models into production applications (e.g. anomaly detection automated reporting agentic AI workflows).
- Develop APIs microservices and user interfaces to expose model capabilities to business users and other systems.
- Design and implement prompt engineering strategies and agentic architectures for autonomous AI workflows.
- Monitor troubleshoot and optimize model performance scalability and reliability in production environments.
- Act as a technical liaison between data science engineering and product teams to ensure seamless integration and delivery.
- Document processes workflows and best practices for model deployment and application integration.
Required qualifications capabilities and skills
- Formal training or certification on software engineering concepts and 5 years applied experience
- Proficiency in Python and experience building APIs/microservices.
- Experience with ML frameworks (e.g. PyTorch TensorFlow Hugging Face) and foundational models (LLMs generative AI).
- Familiarity with prompt engineering and agentic workflows.
- Strong understanding of cloud platforms (AWS GCP Azure) and MLOps practices.
- Excellent communication and collaboration skills.
Preferred qualifications capabilities and skills
- Experience with anomaly detection automated reporting or narrative generation systems.
- Exposure to vector databases retrieval-augmented generation (RAG) or semantic search.
- Experience with containerization (Docker Kubernetes) and CI/CD pipelines.
- Knowledge of security and compliance in AI/ML deployments.
- Experience with Databricks ML Ops.
- Familiarity with regression/classification models and their integration into production systems.
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