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:
1. You will architect scalable APIs and agentic workflows, enabling automation and efficiency across the firm.
2. Architect and deliver production LLM-based systems for text, image, speech, and video applications.
3. Own end-to-end delivery, performance, and continuous improvement of LLM Suite products.
4. 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.
5. Bridge advanced AI research with robust engineering to build innovative, production-ready solutions.
6. Drive results with an entrepreneurial mindset in a fast-paced, high-impact environment.
Required Qualifications, Capabilities, and Skills:
7. Hold a PhD or possess equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative discipline.
8. Demonstrate extensive hands-on experience in ML engineering, with a proven track record of shipping production AI systems.
9. Bring deep expertise in NLP, Computer Vision, and/or Multimodal LLM algorithms, with a strong foundation in statistics, optimization, and ML theory.
10. Apply practical experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed.
11. Communicate complex technical concepts effectively and build trust with stakeholders at all levels.
Preferred Qualifications, Capabilities, and Skills:
12. Design and deploy production ML pipelines using DAG frameworks, including custom operator development and pipeline optimization.
13. Architect and implement high-throughput, low-latency microservices with gRPC, REST, and GraphQL, including protocol buffer schema design, streaming endpoints, and load balancing.
14. 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.
15. Demonstrate deep knowledge of distributed training strategies, memory optimization, and inference acceleration for large-scale multimodal models.
16. Orchestrate advanced agentic workflows, including multi-agent coordination, stateful task management, and integration with enterprise event-driven architectures.