Lead Machine Learning Engineer-MLOps

We are looking for a Senior MLOps engineer to work closely with Data Scientists to build and deploy ML models on a modern MLOps stack. As Lead Machine Learning Engineer on the Recommendation Engine team, you’ll build and maintain pipelines for distributed model training on large compute clusters, batch/real-time model serving, hyperparameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment. Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces. Job 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, performance, and rapid issue resolution. Collaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure. Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions. Required qualifications, capabilities, and skills BS in Computer Science or related Engineering field with 6+ years of experience Or MS degree in Computer Science or related Engineering field with 4+ years experience. Solid knowledge and extensive experience in Python and in cloud computing, preferably AWS Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures Experience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency, x-region resilient applications Deep knowledge and passion for data science fundamentals, training and deploying models Experience in monitoring and observability tools to monitor model input/output and features stats Operational experience in big data/ML tools such as Ray, DuckDB, Spark and in training/inference systems such as Ray, vllm/SGLang Solid grounding in engineering fundamentals and analytical mindset Preferred qualifications, capabilities, and skills Experience with recommendation and personalization systems is a plus. CUDA experience is a big plus Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc] Good knowledge of Databases