Job description

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what's possible together. As a Senior Director of Software Engineering at JPMorganChase within Consumer & Community Banking, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances. Job responsibilities • Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses. • Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code • Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores • Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows • Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback • Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference • Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls • Set engineering standards for agentic AI systems and lead design reviews • Influence roadmap and priorities through technical insight and delivery • Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Required qualifications, capabilities, and skills • Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise • Strong Python engineering skills; experience with PyTorch or TensorFlow • Expertise working with Vector storage systems and designing memory for Agents • Expertise developing long running agents that run autonomously using tools, skills and human in the loop • Proven experience deploying LLM-backed services to production (APIs, microservices) • Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance • Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization • Demonstrated commitment to responsible AI practices and operational excellence • Strong communication and collaboration skills, working across product, risk, legal, and compliance teams • Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data. • Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse. Preferred qualifications, capabilities, and skills • Background operating AI systems in regulated environments (finance, healthcare, etc.) • Experience with prompt engineering and LLM orchestration • Knowledge of safety filters, audit logging, and explainability in production systems • Experience mentoring senior engineers and leading architecture discussions Direct multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains

Requirements

Must-haves

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what's possible together.
Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
Set engineering standards for agentic AI systems and lead design reviews
Influence roadmap and priorities through technical insight and delivery
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
Strong Python engineering skills; experience with PyTorch or TensorFlow
Expertise working with Vector storage systems and designing memory for Agents
Expertise developing long running agents that run autonomously using tools, skills and human in the loop
Proven experience deploying LLM-backed services to production (APIs, microservices)
Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
Demonstrated commitment to responsible AI practices and operational excellence
Strong communication and collaboration skills, working across product, risk, legal, and compliance teams
Background operating AI systems in regulated environments (finance, healthcare, etc.)
Experience with prompt engineering and LLM orchestration
Knowledge of safety filters, audit logging, and explainability in production systems

Nice-to-haves

Preferred qualifications, capabilities, and skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonllmmlci/cdawsazure

Platform: oracle-cloud
Effort: medium