Generative AI Director

JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

Lead the design and delivery of production-grade LLM systems for mission-critical financial products, focusing on scalable APIs, agentic workflows, and end-to-end ownership of LLM Suite products. This role bridges AI research with robust engineering for enterprise-wide AI adoption.

What you'd actually do

You will architect scalable APIs and agentic workflows, enabling automation and efficiency across the firm.
Architect and deliver production LLM-based systems for text, image, speech, and video applications.
Own end-to-end delivery, performance, and continuous improvement of LLM Suite products.
Working closely with ML Engineering, Product Management, and Cloud Engineering, you will ensure our AI solutions are reliable, secure, and built for real business impact.
Bridge advanced AI research with robust engineering to build innovative, production-ready solutions.

Skills

Required

PhD or equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative discipline
Extensive hands-on experience in ML engineering
Proven track record of shipping production AI systems
Deep expertise in NLP, Computer Vision, and/or Multimodal LLM algorithms
Strong foundation in statistics, optimization, and ML theory
Practical experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed
Communicate complex technical concepts effectively
Build trust with stakeholders at all levels

Nice to have

Design and deploy production ML pipelines using DAG frameworks, including custom operator development and pipeline optimization
Architect and implement high-throughput, low-latency microservices with gRPC, REST, and GraphQL, including protocol buffer schema design, streaming endpoints, and load balancing
Hands-on experience with parameter-efficient fine-tuning (LoRA, QLoRA, IA3), model quantization (INT8, FP16, GPTQ), and quantization-aware training for LLMs at scale
Deep knowledge of distributed training strategies, memory optimization, and inference acceleration for large-scale multimodal models
Orchestrate advanced agentic workflows, including multi-agent coordination, stateful task management, and integration with enterprise event-driven architectures

What the JD emphasized

production-grade LLM systems
architect scalable APIs and agentic workflows
Architect and deliver production LLM-based systems
Own end-to-end delivery, performance, and continuous improvement of LLM Suite products
extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems
Apply practical experience implementing distributed, multi-threaded, and scalable applications
Architect and implement high-throughput, low-latency microservices
Orchestrate advanced agentic workflows

Other signals

architect scalable APIs and agentic workflows
architect and deliver production LLM-based systems
own end-to-end delivery, performance, and continuous improvement of LLM Suite products
bridge advanced AI research with robust engineering
demonstrate extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems

Read full job description

Step into a pivotal role at the forefront of JP Morgan’s AI transformation. As part of the Chief Analytics Office (CAO), you’ll drive innovation and shape the future of financial technology. You’ll collaborate with talented teams, architect impactful solutions, and see your work deliver measurable results across the firm. This is your opportunity to influence strategy, build production-grade systems, and unlock new possibilities for our clients and stakeholders. Join us and help define the next era of enterprise AI.

Job Summary: As a Generative AI Executive Director in the Chief Analytics Office, you will lead the design and delivery of production-grade LLM systems that power mission-critical products for thousands of professionals. Your technical leadership will empower teams to innovate and accelerate the adoption of AI at scale.

Job Responsibilities:

You will architect scalable APIs and agentic workflows, enabling automation and efficiency across the firm.
Architect and deliver production LLM-based systems for text, image, speech, and video applications.
Own end-to-end delivery, performance, and continuous improvement of LLM Suite products.
Working closely with ML Engineering, Product Management, and Cloud Engineering, you will ensure our AI solutions are reliable, secure, and built for real business impact.
Bridge advanced AI research with robust engineering to build innovative, production-ready solutions.
Drive results with an entrepreneurial mindset in a fast-paced, high-impact environment.

Required Qualifications, Capabilities, and Skills:

Hold a PhD or possess equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative discipline.
Demonstrate extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems.
Bring deep expertise in NLP, Computer Vision, and/or Multimodal LLM algorithms, with a strong foundation in statistics, optimization, and ML theory.
Apply practical experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed.
Communicate complex technical concepts effectively and build trust with stakeholders at all levels.

Preferred Qualifications, Capabilities, and Skills:

Design and deploy production ML pipelines using DAG frameworks, including custom operator development and pipeline optimization.
Architect and implement high-throughput, low-latency microservices with gRPC, REST, and GraphQL, including protocol buffer schema design, streaming endpoints, and load balancing.
Apply hands-on experience with parameter-efficient fine-tuning (LoRA, QLoRA, IA3), model quantization (INT8, FP16, GPTQ), and quantization-aware training for LLMs at scale.
Demonstrate deep knowledge of distributed training strategies, memory optimization, and inference acceleration for large-scale multimodal models.
Orchestrate advanced agentic workflows, including multi-agent coordination, stateful task management, and integration with enterprise event-driven architectures.

Job Responsibilities:

You will architect scalable APIs and agentic workflows, enabling automation and efficiency across the firm.
Architect and deliver production LLM-based systems for text, image, speech, and video applications.
Own end-to-end delivery, performance, and continuous improvement of LLM Suite products.
Working closely with ML Engineering, Product Management, and Cloud Engineering, you will ensure our AI solutions are reliable, secure, and built for real business impact.
Bridge advanced AI research with robust engineering to build innovative, production-ready solutions.
Drive results with an entrepreneurial mindset in a fast-paced, high-impact environment.

Required Qualifications, Capabilities, and Skills:

Hold a PhD or possess equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative discipline.
Demonstrate extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems.
Bring deep expertise in NLP, Computer Vision, and/or Multimodal LLM algorithms, with a strong foundation in statistics, optimization, and ML theory.
Apply practical experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed.
Communicate complex technical concepts effectively and build trust with stakeholders at all levels.

Preferred Qualifications, Capabilities, and Skills:

Design and deploy production ML pipelines using DAG frameworks, including custom operator development and pipeline optimization.
Architect and implement high-throughput, low-latency microservices with gRPC, REST, and GraphQL, including protocol buffer schema design, streaming endpoints, and load balancing.
Apply hands-on experience with parameter-efficient fine-tuning (LoRA, QLoRA, IA3), model quantization (INT8, FP16, GPTQ), and quantization-aware training for LLMs at scale.
Demonstrate deep knowledge of distributed training strategies, memory optimization, and inference acceleration for large-scale multimodal models.
Orchestrate advanced agentic workflows, including multi-agent coordination, stateful task management, and integration with enterprise event-driven architectures.