JPMorgan Chase

Senior Principal Software Engineer focused on LLM Engineering within JPMorgan Chase's Trust & Safety Fraud Prevention team. The role involves architecting, building, and optimizing model serving solutions, particularly for LLMs and GNNs, across cloud and on-premises environments. Key responsibilities include defining MLOps/LLMOps strategies, driving inference optimization for high throughput and low latency, creating reusable ML engineering frameworks, and ensuring observability, reliability, and cost efficiency in production AI workloads.

Principal Software Engineer at JPMorgan Chase focused on designing, implementing, and scaling AI/ML infrastructure and platforms on AWS. The role involves collaborating with teams, developing and optimizing ML models, leveraging Databricks, and leading the productionalization of Gen AI use cases. Requires strong experience in AI/ML engineering, LLM Ops, model serving, and fine-tuning.

Principal AI/ML and Gen AI Engineer at JPMorgan Chase focused on scaling AI/ML infrastructure and platforms for model serving and fine-tuning, with expertise in LLM Ops and productionalizing Gen AI use cases on AWS and Databricks.

Lead Machine Learning Engineer (MLOps) focused on building and maintaining pipelines for distributed model training, model serving, hyperparameter tuning, monitoring, and production validation for a Recommendation Engine team. The role involves deploying ML models on AWS, supporting personalized experiences across consumer channels, and optimizing inference for LLMs and vector databases.

Director of Software Engineering leading a team to develop and deploy phygital AI solutions leveraging IoT and edge-to-cloud computing. Focus on integrating AI/ML with IoT ecosystems for real-time analytics and scalable solutions.

Senior Lead Site Reliability Engineer role focused on building and optimizing AI/ML platforms and products, including infrastructure, serving, and agentic AI solutions for SRE functions. Requires deep SRE experience applied to AI/ML workloads and LLM inference.

Software Engineer III - AI Reliability Engineer at JPMorgan Chase within Asset and Wealth Management Technology team, focused on enhancing the reliability and resilience of AI systems, particularly large language model serving and training systems. Responsibilities include developing SLOs for AI systems, implementing monitoring, designing high-availability serving infrastructure, championing site reliability culture, developing automated failover and recovery systems, creating AI Incident Response playbooks, leading incident response for critical AI services, building cost optimization systems, engineering for scale and security, and collaborating with ML engineers. Requires formal training/certification in software engineering, proficiency in reliability best practices, observability tools, CI/CD, container orchestration, and understanding AI infrastructure challenges. Preferred qualifications include experience with AI-specific observability tools, AI incident response strategies, AI-centric SLOs/SLAs, and continuous evaluation processes.

Senior Lead Software Engineer role focused on building and optimizing AI/ML infrastructure platforms within a financial services company. The role involves architecting cloud infrastructure, managing ML workloads, CI/CD pipelines, and collaborating with AI teams to meet computational needs. Requires strong software engineering, Kubernetes, cloud, and foundational ML knowledge.

Lead Software Engineer focused on building and optimizing AI/ML infrastructure platforms, including cloud deployment, CI/CD pipelines for ML workloads, and collaboration with AI teams to meet computational needs. The role involves developing secure, scalable infrastructure, monitoring resources, and implementing automation.

Senior Lead Software Engineer focused on building and optimizing AI/ML infrastructure platforms within a large enterprise. The role involves architecting cloud infrastructure, implementing CI/CD for ML workloads, and collaborating with AI teams to meet their computational needs. Requires strong software engineering, cloud, and foundational ML knowledge.

Lead Software Engineer role focused on deploying, monitoring, and managing machine learning models in production. Responsibilities include building infrastructure, automating deployment, optimizing performance, and ensuring the continuous operation of AI systems within a cybersecurity context. Requires expertise in Python, CI/CD, cloud platforms, and containerization.

Lead Software Engineer focused on building and maintaining a scalable ML platform for model training, deployment, and monitoring within a cloud-native DevOps environment. The role involves coding infrastructure with Terraform, Python automation, Kubernetes, CI/CD pipelines, and applying agentic AI/LLM capabilities to DevSecOps use cases.

Senior Data Scientist at JPMorgan Chase focused on building AI/ML solutions for workforce planning, including demand forecasting, capacity optimization, and scheduling. The role involves framing ambiguous problems, selecting appropriate modeling approaches, and productionizing solutions for real-time inference and decision-making within large-scale operations.

Software Engineer role at JPMorgan Chase focused on designing, developing, and deploying enterprise-scale financial applications and data-driven platforms. This role involves leading a team, making architectural decisions for distributed, cloud-native systems, and driving the adoption of data engineering and orchestration tools. A key aspect is overseeing the integration of machine learning solutions, including model development, training, optimization, and deployment using TensorFlow and PyTorch, and implementing MLOps frameworks for lifecycle management. The role also includes implementing service mesh technologies, search and indexing, and managing Agile SDLC, CI/CD, and regulatory compliance.

Lead the design and implementation of advanced AI/ML models and tools for risk assessment and P&L prediction in treasury management within an investment bank. Drive strategic initiatives, own large-scale analytics projects, and automate reporting processes, partnering with technology and business stakeholders. Requires strong Python programming, software engineering, and quantitative finance skills.

Lead Software Engineer focused on building and scaling machine learning platforms and infrastructure at JPMorgan Chase. Responsibilities include designing, developing, and optimizing tools for the end-to-end ML lifecycle, integrating various ML capabilities, and ensuring platform reliability and security. Requires strong software engineering background, experience with ML platforms and MLOps, and Python proficiency.

Executive Director to lead a team of AI/ML specialists in developing horizontal capabilities (APIs, libraries) for AI hosting platforms within a large bank. The role involves hands-on experience in training and deploying models, leading teams, and ensuring responsible AI engineering practices.

Senior Lead Software Engineer focused on building, scaling, and maintaining robust machine learning platforms and infrastructure. The role involves designing and optimizing tools for the end-to-end ML lifecycle, including data engineering, feature management, model training, deployment, monitoring, and serving. Emphasis on secure, high-quality production code, MLOps practices, and collaboration with data scientists and ML engineers to accelerate ML development and operations within an enterprise environment.

As a Principal Software Engineer at JPMorgan Chase, you will provide expertise and engineering excellence to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will execute creative software solutions, design, development, and technical troubleshooting for AI-enabled applications, create complex and scalable coding frameworks, and develop secure and high-quality production code. You will also advise cross-functional teams, serve as a subject matter expert, and contribute to the development of technical methods. The role requires formal training or certification on software engineering concepts, 7+ years of applied experience, DevOps experience, advanced Python skills, practical cloud-native experience, and experience in Computer Science or a related technical field. Experience with building AI platforms is highly preferred.

ML Ops Engineer role focused on deploying, monitoring, and managing machine learning models in production environments within JPMorgan Chase's Cybersecurity team. Responsibilities include building CI/CD pipelines, optimizing infrastructure, and ensuring reliable and efficient AI system performance.

Lead Software Engineer focused on AI/ML Deep Learning and GPU ML Serving within a Commercial and Investment Banking team. Responsibilities include designing, developing, and troubleshooting software solutions, writing production code, producing architecture artifacts, analyzing data, and optimizing deep learning models for inference. The role requires experience with ML systems, Python, ML frameworks, cloud technologies (Docker, Kubernetes), ML model serving frameworks, GPU workloads, low-latency systems, and NoSQL databases. Experience with GPU resource management and microservices architecture is also needed. Preferred qualifications include an advanced degree, proficiency in multiple programming languages, experience with graph neural networks, GPU programming, model monitoring, MLOps tools, and serving large-scale models.

Software Engineer III at JPMorgan Chase focused on AI/ML Deep Learning and GPU ML Serving. The role involves developing, testing, and troubleshooting software solutions, writing production code, producing architecture artifacts, analyzing data, and optimizing deep learning models for production inference. Key responsibilities include deploying and managing GPU workloads in Kubernetes, building scalable, low-latency systems, and partnering with product teams. Requires formal training/certification, 3+ years of applied experience, proficiency in Python and ML frameworks, experience with cloud technologies (Docker, Kubernetes, EKS), ML model serving frameworks, GPU workloads in Kubernetes, and NoSQL databases. Familiarity with modern microservices architecture and leading large-scale system design is also needed. Preferred qualifications include an MS/PhD, Java proficiency, experience with graph neural networks, GPU programming, model monitoring, MLOps tools, and serving large-scale models.

Principal Software Engineer focused on High-Performance Computing (HPC) to optimize AI/ML model training and inference. The role involves creating scalable coding frameworks, developing production code, advising cross-functional teams, and publishing patterns for ML model optimization on various architectures. Requires deep experience in HPC, accelerators, deep learning (LLMs), and AI/ML frameworks, with a focus on performance and scalability for financial services.

Lead Software Engineer focused on building and deploying AI/ML platform infrastructure on AWS for model deployment at scale within a financial institution. The role involves developing APIs for automated retraining, scheduling, endpoint deployments, and autoscaling, as well as implementing model monitoring solutions, with a specific emphasis on LLM monitoring and automated issue correction. The engineer will also engage with clients for support and contribute to disaster recovery and multi-region capabilities.

Principal Software Engineer at JPMorgan Chase within Corporate Technology, focusing on building and enhancing Machine Learning platforms for deploying predictive models at scale. The role involves architecting, designing, and integrating solutions within a large enterprise environment, establishing standards for the ML Platform, and optimizing ML libraries and frameworks. Requires expertise in Java & Spring Boot, ML frameworks, Big Data technologies (Spark, Hadoop), and cloud platforms (Azure, AWS, Databricks).

Senior Manager of Software Engineering for Machine Learning & Cloud within the Corporate Technology - Consumer & Community Bank Risk group at JPMorgan Chase. The role involves leading a team of 10, mentoring technical teams, and driving the development and productionization of ML models using cloud services and MLOps practices. Responsibilities include technical leadership, code development, troubleshooting, and managing ML pipelines.

Lead Software Engineer to build and maintain production-grade ML pipelines and infrastructure at enterprise scale for JPMorgan Chase's MLCOE. This role focuses on designing, developing, and deploying end-to-end ML systems, from data ingestion to serving outputs, with a strong emphasis on AWS services, CI/CD, and performance monitoring.

Lead Software Engineer role focused on building and deploying production-grade machine learning pipelines and systems at enterprise scale within JPMorgan Chase's ML Center of Excellence. The role involves designing, developing, and maintaining software, engineering data pipelines, feeding data into ML models, and deploying complete systems into production environments with a focus on scalability and reliability.

Senior Lead Software Engineer role focused on building and deploying ML solutions into production within an enterprise environment. The role involves creating cloud-based frameworks for hosting ML models, integrating AIML solutions, and ensuring production readiness. Requires strong Python and AWS skills, collaboration with Data Scientists, and experience with the full model development lifecycle.

Lead Software Engineer with expertise in Python and AWS to engineer and deploy ML solutions into production, build cloud-based frameworks for hosting ML models, and integrate ML solutions into complex operational systems. Collaborates with Applied AI/ML group and Data Scientists.

Lead Software Engineer role focused on integrating AI/ML solutions within a financial institution. The role involves developing robust APIs, services, and libraries, architecting cloud infrastructure, and implementing MLOps best practices. Collaboration with Data Scientists and Line of Business teams is key, with a focus on deploying and scaling AI/ML capabilities.

Lead Software Engineer for an MLOps team in a financial institution, focusing on designing, building, and deploying scalable AI/ML solutions, including LLM-powered microservices and generative AI applications. The role involves end-to-end development, monitoring, and maintenance of AI/ML models and systems in a production environment.