Applied AI ML, Senior Associate - LLM SUITE ENGINEERING

Join a team building scalable, production-grade AI capabilities that help teams across the firm deliver better outcomes through reliable automation and decision support. You will work end-to-end, from design to implementation and operational readiness, partnering closely with engineering and product stakeholders. As an Applied AI and Machine Learning Senior Associate at JPMorganChase within LLM Suite Engineering in Enterprise Technology, you will help design and deliver agentic AI platforms and large language model-enabled services for enterprise use cases. You will contribute to architecture decisions, build cloud-native services on AWS, and improve system quality through evaluation and observability. You will help raise engineering standards through strong code reviews, documentation, and collaboration across teams. Job responsibilities Design and implement components of scalable, reliable agentic AI platforms for enterprise workflows Build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration Implement retrieval and context-engineering patterns including embeddings, semantic search, grounding, summarization, and prompt/version management Engineer cloud-native services on AWS using containers, serverless compute, and event-driven messaging patterns Optimize latency, throughput, scalability, caching, context efficiency, and cost across large language model workloads Develop secure, reusable APIs and integrations that connect AI capabilities to enterprise platforms and workflows Implement evaluation, experimentation, regression testing, and observability signals to improve quality and agent behavior over time Partner with product, platform, and engineering teams to translate requirements into resilient, measurable deliverables Contribute to technical standards and code quality through design reviews, documentation, and peer code reviews Required qualifications, capabilities and skills Formal training or certification on applied AI and machine learning concepts and 3+ years applied experience Hands-on experience building and operating production large language model applications, including agentic patterns and tool integrations Strong software engineering skills with experience delivering cloud-native services on AWS using containers and serverless architectures Experience with retrieval-augmented generation approaches, including embeddings and semantic search, and practical context engineering Proficiency building APIs and service integrations with strong attention to reliability, security, and performance Experience establishing or contributing to evaluation, testing, and monitoring practices for AI system quality and reliability Ability to troubleshoot complex issues across distributed systems, including asynchronous workflows and event-driven architectures Strong collaboration skills with the ability to communicate technical decisions and trade-offs clearly to partners Preferred qualifications, capabilities and skills Experience deploying and operating workloads on Kubernetes-based platforms and container orchestration patterns Experience with experimentation frameworks and automated regression testing for large language model quality Familiarity with large language model cost governance and performance optimization techniques (for example, caching and context efficiency) Experience implementing guardrail patterns that support safe, reliable AI behavior in production Experience building reusable platform components and reference implementations adopted by multiple teams