Director of Infrastructure Engineering - AI

At a world-renowned company like JPMorganChase, you can take the lead in shaping the future of technology, unleashing your full potential, and making your mark on the industry.

As a Director of Infrastructure Engineering at JPMorganChase within the Core Engineering Solutions and Architecture group, you manage one large area or multiple smaller ones with advanced technical knowledge to resolve complex issues. Exercise independent judgement within general guidelines and parameters to determine appropriate actions.

Job responsibilities

- Manages technical area(s) within infrastructure engineering, collaborates with others across different technical areas and anticipates the needs of multiple disciplines within the function
- Sets the multi-year strategy and roadmap for AI infrastructure across Consumer & Community Banking, aligned to business priorities and the firm's technology direction
- Sets reuse-first expectations for enterprise-authorized AI adoption within the work environment across the function to accelerate delivery and improve decision quality, with human-in-the-loop validation and appropriate handling of sensitive data
- Manages and implements corporate and divisional operational plans, develops and executes the function objectives, and provides broad direction to teams, team leads, and supervisors with accountability for outcomes, including building, leading, and growing a high-performing organization of engineers and engineering managers through hiring, coaching, performance management, and succession planning
- Manages key senior stakeholders and ensures teams deliver in accordance with compliance standards, risk and security, service level agreements, and business requirements, including partnering with business and product leaders to translate AI use cases into infrastructure requirements prioritized against value and risk
- Represents the business's needs to firm-wide technology and platform teams, shaping roadmaps, negotiating priorities and timelines, and escalating when commitments slip
- Drives multiple large complex projects, processes and initiatives through to delivered outcomes — use cases in production, measurable cost savings, and improved reliability
- Responsible for staffing, budget, and relevant profit and loss, including managing and developing budgets
- Establishes governance standards for AI-assisted workflows used in delivery and operational routines, ensuring traceability/auditability and alignment to resiliency, security, and risk expectations

Required qualifications, capabilities, and skills

- Formal training or certification on infrastructure engineering concepts and 10+ years applied experience
- Experience managing cross-functional teams of infrastructure engineers, with a demonstrated track record of setting technical strategy and delivering it against measurable outcomes
- Demonstrated experience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for infrastructure engineering workflows, including validation habits and awareness of data sensitivity
- Ability to define review and approval expectations for AI-assisted recommendations and ensure outcomes align to resiliency, security, and auditability expectations
- Experience hiring, developing, and recognizing talent, and creating a culture of ownership and engineering excellence
- Extensive knowledge of hardware aligned to infrastructure engineering, including deep background in cloud infrastructure, platform, or site reliability engineering with hands-on depth on at least one major cloud provider (AWS: compute, networking, IAM, managed Kubernetes, observability)
- Experience turning business needs into technical roadmaps and influencing central or shared technology teams not directly managed, with strong cross-team collaboration across engineering, product, security, risk, and finance
- Excellent stakeholder management and executive communication, written and verbal, including presenting to senior leadership, together with budget and vendor management experience and comfort making investment and cost trade-off decisions
- Experience running ML or GenAI workloads in production, such as hosted model APIs (Amazon Bedrock, Azure OpenAI, Google Vertex AI or similar) behind API gateways, with authentication, rate limiting, and resilience, as well as building AI skills, plugins, and agents
- Experience with Kubernetes, including GPU scheduling and auto-scaling, infrastructure as code with Terraform (or equivalent), and CI/CD for infrastructure
- Python and automation fluency to convert manual efforts into automated solutions, paired with observability expertise using tools such as Splunk, Datadog, Prometheus/Grafana, or OpenTelemetry

Preferred qualifications, capabilities, and skills

- Experience with GPU compute – orchestration, scheduling, reclaim policies
- Model serving and optimization: AI Gateways, vLLM, Triton, TensorRT, batching, quantization, latency and throughput
- Distributed training: PyTorch FSDP, DeepSpeed, Megatron-LM; high-speed GPU interconnects (NVLink, InfiniBand)
- RAG and vector databases (OpenSearch, pgvector, Pinecone or similar); agent frameworks (LangChain, LlamaIndex or similar)
- GPU capacity planning and FinOps for AI workloads: cost allocation, chargeback, reserved capacity
- Experience leading an LLM gateway, AI platform, or ML platform organization
- Leadership experience in the financial services industry, including working with risk, compliance, and audit teams