AI Lead Software Engineer

2 weeks ago

Columbus, Ohio, United States JPMorgan Chase Full time $120,000 - $250,000 per year

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.

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