Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank - Digital & Platform Services 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. Drive significant business impact through your capabilities and contributions and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities 

 

Develops and implement a comprehensive strategy for the adoption and integration of Generative AI technologies within the Payments Data Platform.Designs and oversees the implementation of AI-driven solutions, including chatbots and agentic architectures, to enhance customer experience and operational efficiency.Provides leadership and mentorship to the existing team of ML Engineers, fostering a culture of innovation and continuous learning.Works closely with data engineers, software developers, and business stakeholders to ensure alignment of AI initiatives with business goals.Leads the development and deployment of machine learning models for analytics, reporting, and predictive insights, leveraging Databricks and other tools.Establishes and enforces best practices for ML model development, testing, and deployment, ensuring high-quality and reliable outputs.Keeps abreast of the latest advancements in AI and ML technologies and assess their potential impact on the organization.Collaborates with data engineering teams to optimize data pipelines and ensure efficient data processing for ML workloads.Assesses and recommends AI tools and technologies that can enhance the capabilities of the Payments Data Platform.Creates and implements training programs to upskill team members and promote the adoption of AI technologies across the organization.Regularly reports on the progress and impact of AI initiatives to senior management, providing insights and recommendations for future strategies.

 

 Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experienceProven experience in designing, developing, and deploying machine learning models, with a strong understanding of both traditional ML and Generative AI techniques.Expertise in programming languages such as Python, Java, and experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn.Familiarity with big data processing tools and platforms, such as Apache Flink and Databricks, for handling large-scale data analytics and ML workloads.Demonstrated ability to lead and mentor a team of engineers, fostering a collaborative and innovative work environment.Strong strategic thinking and problem-solving skills, with the ability to develop and implement AI strategies that align with business objectives.Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.Experience in managing and delivering complex AI projects on time and within budget, with a focus on quality and impact.Up-to-date knowledge of the latest trends and advancements in AI and ML, with the ability to assess their relevance and applicability to the organization.Strong interpersonal skills and the ability to work effectively with cross-functional teams, including data engineers, software developers, and business leaders.Proactive approach to identifying and solving technical challenges, with a focus on continuous improvement and innovation.

 

 Preferred qualifications, capabilities, and skills 

 

Prior experience working in the financial services industry, particularly in payments or banking, with an understanding of industry-specific challenges and opportunities.Hands-on experience in implementing Generative AI solutions, such as chatbots or agentic architectures, in a production environment.Proficiency in advanced data analytics and statistical methods, with the ability to derive actionable insights from complex datasets.Certifications in AI or ML, such as AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, or similar credentials, demonstrating a commitment to professional development in the field.