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**Applied AI ML Lead - LLM Suite Engineering at JPMorganChase** Lead and deliver scalable, secure AI platforms turning large language model capabilities into reliable business outcomes. You'll partner with product and engineering teams to design architectures, ship reusable capabilities, and raise quality through strong engineering practices and technical leadership. Key responsibilities: - Lead architecture and hands-on delivery of enterprise-grade agentic AI and large language model platforms on AWS. - Design and build production-grade AI systems, including agents, skills, memory patterns, guardrails, and tool-use orchestration. - Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls. - Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior. Required qualifications: - 8+ years of experience in applied AI and machine learning, with formal training or certification. - Proven experience architecting and shipping production large language model applications. - Strong software engineering fundamentals with cloud-native services delivery using containers and serverless designs on AWS. - Experience designing distributed systems, retrieval-augmented generation solutions, and managing prompt lifecycle/versioning. - Demonstrated ability to implement evaluation and monitoring approaches for model quality, reliability, and safe behavior over time. Preferred qualifications: - Experience building standardized evaluation harnesses and experimentation platforms for large language model systems. - Hands-on experience with Kubernetes-based deployment patterns and operational excellence practices for high-availability services. - Experience applying privacy, data minimization, and safe AI guardrail patterns in regulated or high-risk environments. - Familiarity with context-efficiency optimization techniques and cost governance for large language model workloads. Mentor senior engineers and influence engineering direction through code reviews, architecture

Compensation: Not specified
City: Wilmington
Country: United States

Full Job Description

Location: Wilmington, DE, United States

Build and scale production AI platforms that turn large language model capabilities into reliable, secure, and measurable business outcomes. You will partner across product and engineering teams to design architectures, ship reusable capabilities, and raise quality through strong engineering practices and technical leadership.

As an Applied AI and Machine Learning Lead at JPMorganChase within Enterprise Technology, you will lead the architecture and hands-on implementation of scalable large language model systems and agentic AI platforms for enterprise use cases. You will design cloud-native solutions, establish evaluation and observability standards, and drive technical decisions across teams to improve reliability, cost, and developer velocity.

Job responsibilities

Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows

Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration

Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management

Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores

Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls

Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes

Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior

Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle

Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership

Required qualifications, capabilities and skills

Formal training or certification on applied AI and machine learning concepts and 8+ years applied experience

Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns

Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS

Proficiency designing distributed systems with asynchronous workflows, durable messaging, and scalable data access patterns

Experience building retrieval-augmented generation solutions (embeddings, semantic search, grounding) and managing prompt lifecycle/versioning

Demonstrated ability to implement evaluation and monitoring approaches for model quality, reliability, and safe behavior over time

Strong API design skills, including secure integration patterns and reusable platform capability development

Proven technical leadership skills, including mentoring, driving architecture decisions, and influencing cross-functional stakeholders

Preferred qualifications, capabilities and skills

Experience building standardized evaluation harnesses, automated regression suites, and experimentation platforms for large language model systems

Hands-on experience with Kubernetes-based deployment patterns and operational excellence practices for high-availability services

Experience applying privacy, data minimization, and safe AI guardrail patterns in regulated or high-risk environments

Familiarity with context-efficiency optimization techniques and cost governance for large language model workloads

Experience building reusable developer platforms, reference architectures, and technical standards across multiple teams

Lead architecture and delivery of enterprise-grade agentic AI and large language model platforms on AWS.

Full Job Description