AI Lead Software Engineer - Machine Learning

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan

Chase within the Data Products Team, you are 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 firm’s business objectives.

• 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.

• 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, Tensor Flow, 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.

• 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.