Senior Lead Software Engineer - ML- Data Platform

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 experience
• Proven 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.