Senior Lead Software Engineer- AI Platform engineer

Testing Site - JPMC; Jersey City (NJ); On-site; USD 150,000 - 190,000; Full time

Job summary

JPMorgan Chase, within the Corporate Sector, Infrastructure Platforms team, seeks a Senior Lead Software Engineer to drive secure, scalable cloud infrastructure for AI workloads. You will guide multi-team efforts, shape product design, and ensure operational stability across complex distributed systems.

In this role you will collaborate with AI teams, optimize resources, and advance DevOps practices while mentoring engineers in a fast-paced, agile environment in Jersey City, NJ.

Qualifications

• 5+ years of hands-on software engineering experience.
• Strong background in system design, development, testing, and operations.
• Proficient in at least one language such as Python, Go, Java, or C#.

Responsibilities

• Provide technical guidance to cross-functional teams and vendors.
• Develop secure, high-quality production code and review others' work.
• Architect and deploy scalable cloud infrastructure for AI workloads.
• Design CI/CD pipelines for ML workloads and automate deployment.

Skills

Software engineering

System design

Cloud computing

Python/Go/Java/C#

Education

CS/CE/Math degree

Tools

Docker

Kubernetes

AWS

GCP

Azure

Job description

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Sector, Infrastructure Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job Responsibilities

• Provide technical guidance and direction to support business objectives, collaborating with technical teams, contractors, and vendors.
• Develop secure, high-quality production code, and review and debug code written by others.
• Influence product design, application functionality, and technical operations through informed decision-making.
• Advocate for firmwide frameworks, tools, and practices within the Software Development Life Cycle.
• Promote a culture of diversity, equity, inclusion, and respect within the team.
• Architect and deploy secure, scalable cloud infrastructure platforms optimized for AI and machine learning workloads.
• Collaborate with AI teams to translate computational needs into infrastructure requirements.
• Monitor, manage, and optimize cloud resources for performance and cost efficiency.
• Design and implement continuous integration and delivery pipelines for machine learning workloads.
• Develop automation scripts and infrastructure as code to streamline deployment and management tasks.

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering concepts with 5+ years of applied experience.
• Hands-on experience in system design, application development, testing, and operational stability.
• Proficiency in at least one programming language, such as Python, Go, Java, or C#.
• Ability to independently tackle design and functionality problems with minimal oversight.
• Background in Computer Science, Computer Engineering, Mathematics, or a related technical field.
• Strong knowledge of cloud computing delivery models (IaaS, PaaS, SaaS) and deployment models (Public, Private, Hybrid Cloud).
• Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference.
• Experience in solutions design and engineering, containerization (Docker, Kubernetes), and cloud service providers (AWS, Azure, GCP).
• Experience with Infrastructure as Code.
• Deep understanding of cloud component architecture: Microservices, Containers, IaaS, Storage, Security, and routing/switching technologies.

Preferred qualifications, capabilities, and skills

• Foundational understanding of NVIDIA GPU infrastructure software (e.g., DCGM, BCM, Triton Inference).
• Hands-on experience with machine learning frameworks such as PyTorch and TensorBoard.
• Proficiency with observability tools like Prometheus and Grafana.
• Experience in ML Ops and related tooling, including MLflow.
• Background in high performance computing and ML frameworks (e.g., vLLM, Ray.io, Slurm).
• Strong knowledge of network architecture, database programming (SQL/NoSQL), and data modeling.
• Familiarity with cloud data services, big data processing tools, and Linux environments (scripting and administration).