Applied AI ML - India Lead

Role overview

Applied AI ML - India Lead at JP Morgan

In this role, you will partner closely with product, engineering, data, and control stakeholders to identify high-value machine learning use cases and take them from concept to production.

Responsibilities

• You will balance delivery speed with security, resiliency, and responsible use expectations, ensuring solutions are scalable and supportable.
• Develop high-quality production code in Python and perform thorough code reviews to improve correctness, security, and maintainability.
• Build and operationalize machine learning models, including generative approaches where applicable, from experimentation through deployment and ongoing support.
• Drive workflow automation opportunities using machine learning to improve customer or employee experiences while maintaining appropriate controls.
• Lead applied artificial intelligence and machine learning to build secure, scalable products for consumer and community banking

Required qualifications

• Required qualifications, capabilities, and skills Formal training or certification in software engineering concepts and 5+ years of applied software engineering experience.

Preferred qualifications and skills

• Preferred qualifications, capabilities, and skills Experience designing or implementing agent-based artificial intelligence solutions (for example, tool-using or multi-step reasoning workflows).

Employment details

• Location: Bengaluru [Karnataka] [India]

Application details

• Define evaluation, testing, and monitoring practices for model and application performance, reliability, and drift to sustain production outcomes.
• Hands-on experience delivering system design, application development, testing, and production support for customer- or business-critical systems.

Additional role details

• Shape the future of customer-facing financial experiences by leading applied artificial intelligence and machine learning solutions that deliver real business impact.
• Join a team that values engineering excellence, modern architecture, and strong collaboration across product, data, and risk partners.
• Bring your expertise to a role with broad mobility, meaningful technical ownership, and opportunities to influence platform standards and adoption.
• As an Applied AI ML Lead at JPMorgan Chase within the Consumer and Community Banking - Architecture and Engineering team, you are an integral part of a 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 delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
• You will also help teams adopt repeatable patterns for model development, deployment, and monitoring.
• Success requires strong technical judgment, clear communication, and the ability to lead through influence in a fast-moving environment.
• Job Responsibilities Design end-to-end software and machine learning solutions that are secure, resilient, and scalable for production use.
• Automate recurring remediation and operational tasks to improve stability, reduce incidents, and streamline support.
• Lead technical solutioning across multiple components and services, aligning architecture decisions with business requirements and platform constraints.
• Evaluate external vendors, startups, and internal solutions by probing architecture, technical fit, and integration feasibility within existing systems.
• Mentor engineers through communities of practice and day-to-day guidance, reinforcing an inclusive, respectful, and high-performing team culture.
• Professional experience developing in Python, including building and supporting machine learning workloads.
• Experience implementing automation and continuous delivery practices in a production engineering environment.
• Working knowledge of the full software development life cycle, including design, build, test, release, and operate.
• Experience building or integrating machine learning solutions, including generative model use cases in production or pre-production environments.
• Experience working with cloud-native technologies and deploying workloads to a public cloud environment (for example, Amazon Web Services).
• Experience working in financial services technology or similarly regulated environments with security and control expectations.
• Experience fine-tuning or adapting language models, including small language models and large reasoning models, for domain-specific use cases.
• Familiarity with model evaluation techniques for generative solutions (for example, automated quality checks and human-in-the-loop review patterns).
• Experience partnering with governance, risk, or control stakeholders on responsible model deployment and ongoing monitoring.