We're looking for a hands-on platform engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies.
As a Platform Engineer - Senior VP at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you will design and own the platform, tooling, and infrastructure that our agentic AI and machine learning products depend on. As the team moves from shipping individual use cases to running multiple production platforms and the production tail of new use cases, you will set the engineering standard for deployment, scalability, security, and reliability, and lead the practices that keep our services running.
This is a Senior VP-level role and an integral part of the IPB Tech AIML team, reporting to the Head of AI, IPB Tech.
Job responsibilities
- Owns the design and build of the team's platform: deployment pipelines, model serving, containerisation, orchestration, and environment management
- Sets the standard for reliability, observability, and operational excellence across the team's production AI/ML services
- Builds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quickly
- Implements platform-level security, secrets management, and access controls to firm-wide standards
- Partners with data and AI engineers to productionise models, and coordinates with external infrastructure functions to reduce operational dependency and risk
- Leads capacity, cost, and performance management for the team's compute and inference workloads
- Mentors platform and DevOps engineers and sets engineering standards through design and code review
- Champions the firm's culture of diversity, Opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Advanced proficiency in Python and infrastructure-as-code, with modern software engineering practices
- Deep hands-on experience with Kubernetes, containerisation, and cloud-native deployment patterns
- Strong CI/CD experience and a track record of building deployment and release automation
- Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps)
- Practical experience with observability tooling (metrics, logging, tracing) and production incident response
- Experience operating ML / LLM workloads in production (LLMOps, inference reliability, cost/performance management)
- Strong communication skills and the ability to set standards other engineers adopt
- Industry-recognised container / Kubernetes certification (e.g., Certified Kubernetes Application Developer (CKAD), or similar)
- Master's degree in Computer Science, Engineering, or a related technical field (or equivalent applied experience)
Preferred qualifications, capabilities, and skills
- Site Reliability Engineering (SRE) experience and familiarity with reliability practices (SLOs, error budgets)
- Experience within financial services technology
- Familiarity with JPM-internal platform, cloud, and AI/ML infrastructure for internal candidates