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 

Solid fundamentals in cloud computing, preferably AWS 

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 

Solid grounding in engineering fundamentals and analytical mindset 

Action Oriented  and iterative development 

 

Preferred qualifications, capabilities, and skills 

 

Experience with recommendation and personalization systems is a plus. 

Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc] 

Good knowledge of Databases