Senior Lead Software Engineer - AI/ML Platform

Salary: $61,000 - 101,000 per year Requirements:

• Formal training or certification in software engineering concepts, with at least 5 years of applied experience.
• Experience delivering secure, production-quality code in Python or Java.
• Strong understanding of distributed systems, microservices, and platform architecture and design.
• Proven ability to architect and operate cloud-native infrastructure on AWS, including compute, networking, storage, and security, as well as experience with other major cloud providers.
• Demonstrated expertise with Terraform for infrastructure as code in large-scale cloud environments.
• Hands-on experience with Docker and Kubernetes, including operating AWS EKS.
• Experience building or supporting production AI/ML platforms for training, deployment, and model serving or inference, including GPU infrastructure and tooling.
• Strong DevOps and platform engineering practices, including CI/CD, release automation, automated testing, and observability across monitoring, logging, and tracing.
• Experience with SQL or NoSQL databases and data integration, plus strong Linux, scripting, and networking fundamentals.
• Experience leading the effective use of enterprise-authorized AI-assisted software development tools for activities such as coding, code review, test acceleration, and troubleshooting, and setting expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resilience and security expectations; experience coaching senior engineers or leads on compliant practices and controls.
• Preferred: Proficiency in Go or Python for automation, tooling, or platform services.
• Preferred: Experience with MLOps tools such as Kubeflow, MLflow, or similar lifecycle management platforms.
• Preferred: Working knowledge of ML frameworks such as PyTorch, TensorFlow, Hugging Face, or scikit-learn for model integration and operationalization.
• Preferred: Exposure to multi-cloud or hybrid cloud architectures and platform portability strategies.

• Build and maintain reusable AI/ML platform infrastructure and shared services that support development, deployment, and operations at scale.
• Architect, deploy, and operate secure cloud and container environments for training and inference, including GPU-intensive workloads.
• Develop platform tooling, automation, and infrastructure-as-code solutions to streamline model deployment, environment provisioning, release management, and operational support.
• Build and maintain production-grade services, APIs, SDK integrations, and workflows for model training, serving, evaluation pipelines, and AI application lifecycle management.
• Partner with data science, ML engineering, and application teams to translate model and compute needs into platform standards and deployment patterns.
• Improve platform reliability, scalability, latency, and cost through orchestration, scheduling, and hardware acceleration.
• Establish operational practices for monitoring, logging, observability, access controls, incident response, and production troubleshooting.
• Support enterprise LLM operations, including fine-tuning workflows, inference optimization, and evaluation, and contribute to documentation and engineering standards.
• Lead the adoption and governance of approved AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes. Establish measurable validation standards, including secure coding, peer review, and automated testing, and promote reusable patterns and automation across the SDLC/TLM toolchain.
• Apply knowledge of the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to increase the value of automation at scale.

• AI
• AWS
• Architect
• CI/CD
• Cloud
• DevOps
• Docker
• Fine-tuning
• Hardware
• Support
• Java
• Kubeflow
• Kubernetes
• LLM
• Linux
• Machine Learning
• MLflow
• MLOps
• Model Serving
• Model Training
• NoSQL
• PyTorch
• Python
• SQL
• Security
• TensorFlow
• Terraform
• microservices
• Marketing

More:

We are hiring a Senior Lead Software Engineer for our Corporate AIML Data Platforms team at JPMorgan Chase. You will be part of an agile team building and operating foundational cloud infrastructure that helps data scientists and machine learning engineers develop, train, and deploy solutions across the firm. Our corporate teams support the success of our businesses, clients, customers, and employees. JPMorganChase is a longstanding financial institution serving consumers, small businesses, and corporate, institutional, and government clients, with businesses spanning investment banking, consumer and commercial banking, transaction processing, and asset management. We offer a competitive total rewards package, with compensation based on role, experience, skills, and location; eligible roles may include incentive compensation. Benefits, subject to eligibility, include health care, wellness resources, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We value diversity and inclusion and provide reasonable accommodations for applicants and employees.

last updated 42 week of 2026