What are the responsibilities and job description for the AI Lead Software Engineer - Machine Learning position at JPMorgan Chase?
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 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.