Lead Software Engineer-AI Platform Engineer Job Jersey City area,New Jersey USA,Software Development

Lead Software Engineer-AI Platform Engineer

Jersey City, NJ, United States

Job Identification:

Job Category: Software Engineering

Business Unit: Corporate Sector

Posting Date: 15/09/2025, 22:16

Location: 575 Washington Blvd, Jersey City, NJ, 07310, US

Job Schedule: Full time

Base Pay/Salary: Jersey City, NJ $-$

Job Description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector, specifically as a part of the Infrastructure Platforms team, you will play a crucial role in an agile team committed to enhancing, creating, and delivering high-quality technology products in a secure, stable, and scalable manner.

Your role as a vital technical contributor will involve developing critical technology solutions across numerous technical domains within various business functions, all aimed at supporting the firm's business goals.

Responsibilities

Execute creative software solutions, including design, development, and technical troubleshooting, with the ability to think beyond conventional approaches to build solutions or resolve technical problems.
Develop secure, high-quality production code, and review and debug code written by others.
Identify opportunities to eliminate or automate the remediation of recurring issues to enhance the overall operational stability of software applications and systems.
Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented assessments of architectural designs, technical credentials, and their applicability within existing systems and information architecture.
Lead communities of practice across Software Engineering to promote awareness and adoption of new and leading-edge technologies.
Contribute to a team culture of diversity, equity, inclusion, and respect.
Develop and deploy cloud infrastructure platforms that are secure, scalable, and optimized for AI and machine learning workloads.
Collaborate with AI teams to understand computational needs and translate these into infrastructure requirements.
Monitor, manage, and optimize cloud resources to maximize performance and minimize costs.
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 practical experience in delivering system design, application development, testing, and ensuring operational stability.
Advanced proficiency in one or more programming languages such as Python and/or Golang.
Proficiency in automation and continuous delivery methods.
Proficient in all aspects of the Software Development Life Cycle.
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
Proficiency in Linux environments, including scripting and administration.
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 (Terraform, Cloud Formation) and automation tools (Ansible, Chef, Puppet).
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., NVIDIA DCGM, BCM, Triton Inference).
Hands‑on experience with ML frameworks such as PyTorch, Tensor Board.
Experience with observability tools like Prometheus, Grafana.
Experience in ML Ops and associated tooling like MLflow.
Experience with High Performance Computing and Machine Learning frameworks such as vLLM, Ray.io, Slurm.
Strong background in network architecture, database…