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 JPMorganChase 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.

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 experienceProficiency 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.