Lead Software Engineer (AI Platform) - Vice President
Salary not listed • New York, NY, United States • Full-time
Posted 29d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
JPMorgan Chase & Co. is seeking an AI Platform Engineer – Vice President to join the Global Technology Applied Research (GTAR) center. This role will engineer, operate, and evolve the shared platforms that enable researchers to develop, evaluate, and mature AI solutions in secure, scalable, firm-approved environments. You will own the cloud and compute foundation for AI research workloads, build reusable platform services and automation, and partner with researchers and enterprise technology teams to ensure experimentation is reproducible, reliable, well-controlled, and ready for downstream integration.
Responsibilities
- Own the architecture, technical roadmap, and day-to-day engineering of the AI research platform.
- Design and operate secure, scalable cloud and CPU/GPU infrastructure, including clusters, workload scheduling, storage, networking, capacity planning, performance, and cost.
- Build reproducible, self-service research environments and platform automation using standardized container images, GPU software stacks, dependency and artifact management, infrastructure-as-code, CI/CD, and orchestration.
- Partner with researchers to translate requirements from LLM, agentic AI, model evaluation, retrieval, inference, and accelerator benchmarking projects into dependable platform capabilities.
- Prepare research prototypes for downstream integration through packaging, service interfaces, and operational-readiness reviews.
- Assess and onboard new AI infrastructure technologies, cloud services, accelerators, and developer tooling through practical benchmarks, architecture reviews, and controlled pilots.
- Maintain reliable, well-controlled platforms through observability, incident response, patching and lifecycle management, access and data controls, risk and control evidence, operational documentation, and runbooks.
Requirements
- Master's degree in computer science, software engineering, computer engineering, information systems, or a related technical field, plus at least 4 years of relevant industry experience building or operating cloud, platform, or infrastructure systems.
- Hands-on experience operating production-grade environments, including EC2, networking, storage, monitoring, and cost management.
- Strong knowledge of Linux systems, containers, networking, storage, and distributed systems, with experience in orchestration or workload-scheduling technologies.
- Experience with infrastructure-as-code and delivery automation using tools such as Terraform or CloudFormation, configuration management, and CI/CD pipelines.
- Proficiency in Python and shell scripting, with sound software engineering practices for building maintainable automation, services, and platform integrations.
- Working knowledge of AI/ML development workflows and demonstrated ability to translate research requirements into platform designs, communicate technical trade-offs, manage delivery dependencies, and drive risk and control items to closure.
- Experience in one or more of the following domains: cloud and accelerated compute platforms (e.g., AWS, CPU/GPU infrastructure, autoscaling, workload scheduling, resource quotas, capacity planning); research environments and developer platforms (e.g., containers, Jupyter, package and dependency management, artifact repositories, experiment tracking, self-service tooling); AI workload enablement (e.g., LLM inference and evaluation, agentic workflows, retrieval pipelines, model-serving infrastructure, distributed processing); reliability and operations (e.g., observability, service-level objectives, incident response, resilience, backup and recovery, operational readiness).