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$202,800–$255,062.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As an Executive Director Machine Learning Engineer-MLOps on the Recommendation Engine team, you will collaborate with Data Scientists to build and deploy machine learning models within a high-throughput, low-latency environment. You will be responsible for implementing fine-tuning and reinforcement learning algorithms on large compute clusters, managing real-time and batch model serving systems, and performing hyper-parameter tuning at scale. Your daily work involves building distributed training pipelines on GPU-enabled clusters, optimizing vector databases, and establishing monitoring and observability pipelines. You will utilize Python, AWS, vLLM, Ray, and CUDA to deploy open-weight large language models while applying quantization techniques like PTQ and AWQ. The role focuses on the technical challenges of serving transformer-based models and developing infrastructure for personalization and insights within a complex banking ecosystem involving travel, merchant offers, and dining services.
Build and maintain robust pipelines for distributed training on GPU-enabled clusters.
Develop and manage high-volume real-time and batch inference systems.
Implement quantization techniques to deploy large language models on modern serving stacks.
Manage and optimize vector databases to support advanced AI applications.
Establish comprehensive monitoring and observability pipelines for system health and performance.
Perform hyper-parameter tuning at scale for model development and deployment.
Integrate new technologies into existing infrastructure to improve production systems.
What we're looking for
BS in Computer Science or related field with 10+ years experience, or MS degree with 6+ years experience.
Extensive experience in Python and cloud computing, specifically within the AWS environment.
Understanding of quantization techniques like PTQ and AWQ to accelerate LLM inference on GPU architectures.
Solid understanding of Transformer models and the challenges involved in serving large transformer-based models.
Solid understanding of ML training, especially reinforcement learning algorithms such as GRPO and DAPO.
Experience in systems engineering fundamentals including caching, CUDA, autoscaling, high throughput, low latency, and x-region resilient applications.
Experience with monitoring and observability tools to monitor model input/output and feature statistics.
Experience with recommendation systems (preferred); experience with Ray, vLLM, RL libraries, or Kubernetes (preferred).
AI
ML
RAG
LLMs
Generative AI
Distributed Systems
Platform Engineering
Context Orchestration
Memory Systems
Retrieval Systems
Real-time Inference
Observability
System Optimization
Agent-based Systems