Lead Machine Learning Engineer-MLOps

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage pipelines for high-throughput, real-time inference as well as batch inference, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases to support advanced AI and machine learning applications.
• Establish and maintain comprehensive monitoring and observability pipelines to ensure system health and rapid issue resolution.
• Collaborate with cross-functional teams to integrate new technologies and define scalable technical solutions.

Requirements

• BS in Computer Science or related Engineering field with 6+ years of experience, or MS degree with 4+ years of experience.
• Extensive experience in Python and cloud computing, specifically AWS.
• Understanding of quantization techniques (e.g., PTQ, AWQ) used to accelerate LLM inference on GPU architectures.
• Experience in systems engineering fundamentals including caching, CUDA, autoscaling, and high-throughput/low-latency applications.
• Operational experience with big data and ML tools such as Ray, DuckDB, Spark, and inference systems like vllm or SGLang.
• Experience using monitoring and observability tools to track model input/output and feature statistics.

Preferred Qualifications

• Experience with recommendation and personalization systems.
• Hands-on experience with CUDA.
• Proficiency with containers (Docker) and orchestration systems (Kubernetes, ECS).
• Experience with DAG orchestration tools such as Airflow or Kubeflow.
• Strong knowledge of database management.

About the Company

JPMorganChase is a leading global financial institution offering innovative solutions to millions of consumers, small businesses, and prominent corporate and institutional clients. We leverage state-of-the-art machine learning architectures to power personalized experiences across our banking channels, integrating traditional services with travel, shopping, and dining ecosystems.