JPMorganChase is looking to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way by integrating ML models into production applications and exposing their capabilities to business users and other systems.
Requirements
- 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.
- 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.
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.
Other
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Excellent communication and collaboration skills.
- 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.
