AI Applications Developer

JPMorgan Chase & Co.; Taguig; On-site; PHP 1,200,000 - 1,600,000; Full time

Job summary

JPMorgan Chase & Co. is seeking an AI Applications Developer in the Intelligent Applications team to design, build, and deliver production-grade AI solutions. You will work with cross-functional teams to move from discovery to production, focusing on security, scalability, and responsible use.

You will improve reliability through systematic evaluation, monitoring, and iterative enhancement, contributing to reusable patterns, runbooks, and documentation for support across teams.

Qualifications

• Proven experience building and deploying modern production applications.
• Hands-on Python development, writing maintainable, testable code.
• Experience creating intelligent applications using large language models with prompt design and evaluation.
• Experience implementing agent frameworks (e.g., Google Agent Development Kit).
• Experience with retrieval-augmented generation, indexing, grounding, and citations.
• Experience with containerized development and deployment (Docker).
• Experience with cloud services for data and applications workloads.
• Strong secure development practices: IAM, data handling, auditability.

Responsibilities

• Design and deliver production-grade applications powered by LLMs and analytics.
• Build agent-based systems planning and executing complex workflows across tools and services.
• Develop and optimize prompts, evaluation methods, and guardrails for reliability.
• Implement retrieval-augmented generation patterns grounded in trusted data sources.
• Integrate apps with cloud services, data stores, and APIs for end-to-end workflows.
• Collaborate with product, data, engineering, and governance teams to define requirements.
• Apply secure-by-design practices including access controls and data protection.
• Create automated tests, monitoring, and observability for production quality.

Skills

Python development

LLM applications design

Prompt design

Agent frameworks

Retrieval-augmented generation

Security by design

Analytical thinking

Problem solving

Tools

Docker

Kubernetes

Cloud services (AWS/GCP/Azure)

Serverless architectures

CI/CD tooling

Job description

Help shape how intelligent technology delivers real outcomes across the business. You will turn challenging problems into secure, scalable applications powered by artificial intelligence and advanced analytics. You will partner with experts across product, data, engineering, and governance to deliver solutions that matter. If you enjoy moving from concept to production and driving measurable impact, you will thrive here.

As an AI Applications Developer in the Intelligent applications team, you will design, build, and deliver production-grade solutions that apply model-driven capabilities to real business needs. You will partner with cross-functional teams to move from discovery through launch, with a focus on security, scalability, and responsible use. You will help improve reliability through systematic evaluation, monitoring, and iterative enhancement. You will contribute to shared patterns, documentation, and engineering practices that enable reuse and supportability.

Job responsibilities

• Design and deliver production-grade applications powered by large language models and advanced analytics
• Build agent-based systems that plan, decompose, and execute complex workflows across tools and services
• Develop and optimize prompts, evaluation methods, and guardrails to improve reliability and user outcomes
• Implement retrieval-augmented generation patterns to ground model outputs in trusted data sources
• Integrate applications with cloud services, data stores, and APIs to support end-to-end workflows
• Partner with product, data, engineering, and governance stakeholders to define requirements and deliver solutions
• Apply secure-by-design practices, including access controls, data protection, and auditability throughout development
• Create automated tests, monitoring, and observability to improve quality, safety, and operability in production
• Document architectures, decisions, and runbooks to enable supportability and reuse across teams
• Troubleshoot performance, latency, and model quality issues and deliver iterative improvements
• Stay current on advances in generative and agent-based approaches and translate them into practical solutions

Required qualifications, capabilities and skills

• Professional experience building and deploying modern applications in a production environment
• Hands-on Python development experience, including writing maintainable, testable code
• Demonstrated experience building intelligent applications using large language models, including prompt design and systematic evaluation
• Demonstrated experience implementing agent frameworks (for example, Google Agent Development Kit or similar)
• Demonstrated experience with retrieval-augmented generation solutions, including indexing, retrieval strategies, grounding, and citations
• Hands-on experience with containerized development and deployment (for example, Docker and orchestration platforms)
• Hands-on experience with cloud services for data and application workloads (for example, managed compute, storage, databases, and serverless services)
• Strong understanding of secure development practices, including identity and access controls and data handling principles
• Ability to translate ambiguous business problems into technical designs, milestones, and measurable outcomes
• Strong analytical and problem-solving skills, including root-cause analysis in production systems

Preferred qualifications, capabilities and skills

• Experience deploying and operating solutions on Kubernetes (for example, Amazon Elastic Kubernetes Service)
• Experience with managed model platforms and orchestration patterns (for example, Amazon Bedrock or similar)
• Experience building data pipelines and analytics workflows (for example, AWS Glue and Amazon Athena)
• Experience with relational database design and performance tuning (for example, Amazon Relational Database Service)
• Experience implementing model fine-tuning or adaptation techniques with clear evaluation and governance practices
• Experience partnering with risk, controls, privacy, and compliance stakeholders on model-enabled products
• Experience mentoring engineers and establishing team development standards and reusable patterns