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 Consumer and community banking- Architecture and Engineering, 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:

• Develop and productionize high-quality machine learning models, services, and platforms to create significant technological and business impact.
• Design and implement scalable and reliable data processing pipelines, performing analysis and deriving insights to optimize business outcomes.
• Deploy machine learning models using LLMs for Generative AI applications, including text generation, classification, and question answering.
• Collaborate with cross-functional teams to identify business requirements and develop data-driven solutions using GenAI technologies.
• Build and maintain data lakes and data processing workflows using Databricks to support machine learning operations.
• Implement data visualization and analytics solutions using ThoughtSpot to provide actionable insights to stakeholders.
• Conduct research on prompt engineering techniques to enhance the performance of LLM-based models.
• Analyze and interpret complex datasets to evaluate model performance and identify areas for improvement.
• Communicate technical concepts and results effectively to both technical and non-technical stakeholders.
• Develop end-to-end ML pipelines for real-time and batch predictions and integrate them with existing applications.
• Champion a DevOps model and support the maturity of the ML development life cycle (MDLC).

Required Qualifications, Capabilities, and Skills:

• Formal training or certification on software engineering concepts and 5+ years of applied experience
• Strong programming skills in Python, with experience in machine learning frameworks such as PyTorch or TensorFlow.
• Experience in building and managing data lakes and data processing workflows using Databricks.
• Experience in building AI Agents and MCP Servers
• Proficiency in using GenAI models (OpenAI or similar) to solve business problems.
• Solid understanding of data structures, algorithms, and machine learning concepts.
• Experience in NLP and deep learning, with recent exposure to prompt engineering on LLMs.
• Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders.
• Experience with big data technologies (Hadoop, Spark, etc.) and cloud-based technologies (AWS, Azure, GCP).
• Hands-on experience with MLOps tools and practices, ensuring seamless integration of models into production environments.

Preferred Qualifications, Capabilities, and Skills:

• Experience in developing and deploying production-grade NLP models.
• Familiarity with data visualization tools like ThoughtSpot.
• Experience in developing APIs and integrating machine learning models into software applications.
• Knowledge of modern development technologies and tools such as Agile, CI/CD, Git, Terraform, and Jenkins.
• Experience with Enterprise Cloud infrastructure and monitoring tools like Data Dog, Prometheus, Splunk, Elasticsearch, and Grafana.