Applied AI ML - India Lead

JPMorganChase · Bengaluru · 5+ yrs experience · Posted 2026-07-28

Tech stack: Python

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About the role

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.
Responsibilities:
- Design end-to-end software and machine learning solutions that are secure, resilient, and scalable for production use.
- 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.
- Define evaluation, testing, and monitoring practices for model and application performance, reliability, and drift to sustain production outcomes.
- 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.
- Drive workflow automation opportunities using machine learning to improve customer or employee experiences while maintaining appropriate controls.
- 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.
Qualifications:
- Formal training or certification in software engineering concepts and 5+ years of applied software engineering experience.
- Hands-on experience delivering system design, application development, testing, and production support for customer- or business-critical systems.
- 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 designing or implementing agent-based artificial intelligence solutions (for example, tool-using or multi-step reasoning workflows).
- 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.

Qualifications

- Formal training or certification in software engineering concepts and 5+ years of applied software engineering experience.
- Hands-on experience delivering system design, application development, testing, and production support for customer- or business-critical systems.
- 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 designing or implementing agent-based artificial intelligence solutions (for example, tool-using or multi-step reasoning workflows).
- 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.

Responsibilities

- Design end-to-end software and machine learning solutions that are secure, resilient, and scalable for production use.
- 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.
- Define evaluation, testing, and monitoring practices for model and application performance, reliability, and drift to sustain production outcomes.
- 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.
- Drive workflow automation opportunities using machine learning to improve customer or employee experiences while maintaining appropriate controls.
- 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.

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