Software Engineer Jobs in Jersey City, NJ

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

Leads software engineering efforts within Credit Risk Technology, including system design, application development, testing, troubleshooting, code review, and production stability. Modernizes the technology stack, develops secure and scalable code, automates recurring remediation, evaluates vendor and architectural solutions, and promotes engineering best practices. Requires advanced Java or Python skills, strong relational database and Databricks expertise, and experience working in large corporate environments.

Leads software engineering within agile teams by designing, developing, testing, and troubleshooting secure, scalable Java or Python applications. Builds blockchain solutions, including Ethereum smart contracts and decentralized applications, and uses AWS to deploy cloud systems. Contributes to architecture, resiliency, CI/CD, data analysis, and production stability. Drives responsible adoption of AI-assisted engineering tools, establishes validation standards, and coaches engineers on secure and compliant usage.

Leads architecture and hands-on development of scalable ML and generative AI platforms. Builds Python APIs and SDKs, Databricks data and ML workflows, agentic AI capabilities, model integrations, infrastructure automation, and CI/CD pipelines. Ensures reliability, monitoring, security, performance, and cost efficiency while mentoring engineers, partnering with data science and product teams, and establishing AI-assisted engineering quality standards.

Leads full-stack Java software engineering within Credit Risk Technology. Designs, develops, tests, debugs, and maintains secure, scalable production applications; reviews code; improves automation and operational stability; and promotes responsible AI-assisted engineering practices. Evaluates vendor and architectural solutions, coaches team members, and contributes across the software development lifecycle using agile, cloud-native, resilient, and secure delivery practices.

Leads software engineering efforts for a data platform within an agile corporate technology team. Designs, develops, tests, debugs, and maintains secure, scalable production systems; reviews code; improves operational stability through automation; and promotes responsible AI-assisted development practices. Evaluates vendors and architectural solutions, coaches engineers on secure AI use, and applies cloud-native technologies, software development lifecycle practices, resiliency, and security standards in financial services systems.

Leads full-stack software engineering for wholesale credit risk technology. Designs, develops, tests, debugs, and reviews secure production code; improves application stability through automation and remediation; evaluates architectural solutions with vendors and internal teams; promotes AI-assisted engineering practices, modern technologies, and software engineering best practices across communities of practice.

Leads software engineering for AI/ML data platforms, providing technical direction, developing secure production code, and driving scalable SDLC automation. Establishes governance and validation standards for AI-assisted engineering, coaches teams on responsible AI use, and contributes to system design, application functionality, cloud-native operations, testing, and reliability. Preferred experience includes Go or Python, MLOps platforms, ML frameworks, and multi-cloud or hybrid architectures.

Designs, develops, and supports scalable, highly available software applications across digital channels. Responsibilities include architectural design, coding, code reviews, agile Scrum participation, story creation, technical solution evaluation, production monitoring, and mentoring engineers. The role requires full-stack development using React and Python, cloud and distributed systems experience, CI/CD deployment across OpenShift and GCP, database and API expertise, and strong software quality, security, and performance practices.

Design, develop, and maintain network datapath monitoring and observability infrastructure for GPU cloud services. Build real-time network telemetry and analytics pipelines across host networking, smart NICs, and overlay/underlay networks. Collaborate with DevOps, production, and data platform teams; troubleshoot Kubernetes and cloud networking issues; participate in on-call support, code reviews, architecture decisions, and continuous infrastructure improvements.

Design, build, and operate secure sandboxed runtime environments for Kubernetes and GPU-accelerated workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, container runtime, virtualization, and GPU driver architecture. Collaborate with security, platform, and infrastructure teams to establish runtime isolation and performance standards for multi-tenant systems.

The Senior Software Engineer will design and support Kubernetes infrastructure, develop applications in Go, and enhance platform reliability, performance, and observability in CoreWeave's AI-focused environment.

Principal Software Engineer responsible for designing scalable software frameworks and secure production code, governing enterprise Databricks platforms, and leading agentic AI-enabled engineering workflows. The role advises cross-functional teams, establishes responsible AI, security, resiliency, and governance standards, manages competing portfolios, and influences senior stakeholders. It requires deep expertise in Python, AWS, Terraform, Databricks architecture and operations, enterprise data platforms, and global data governance.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Leads architecture and delivery of workflow automation, AI/ML pipelines, and cloud-native solutions across AWS, Azure, and GCP. Develops production-grade software, establishes Terraform and CI/CD standards, operationalizes model lifecycles, and promotes secure, resilient, cost-effective engineering practices. Scales enterprise AI-assisted development and SDLC automation across teams through governance, quality controls, responsible AI practices, and measurable delivery improvements.

Designs scalable software frameworks and production systems for high-performance computing, optimizing AI/ML model training and inference across compute architectures. Leads technical design reviews, benchmarking, telemetry, secure coding, and operational improvements. Develops enterprise agentic AI engineering workflows with governance, validation, auditability, security, and resiliency guardrails. Mentors engineers and drives adoption of HPC, AI/ML, and automation practices across teams.

Develop and execute software test plans for enterprise storage management applications. Validate storage array solutions end to end, maintain QA processes and CI/CD automation, perform API integration testing, and troubleshoot customer issues. Collaborate with engineering and field teams on debugging, issue reproduction, risk management, and feature delivery. The role requires experience with Python, JavaScript, Shell scripting, server, storage, networking, Agile QA methodologies, and Linux-based software testing.

Provides technical leadership and hands-on engineering for liquidity risk applications. Builds enterprise services and APIs with Python, develops Snowflake data solutions using complex SQL, and delivers workflows spanning UI, APIs, and data warehouses. Responsibilities include solution design, deployment, mentoring engineers, risk and control management, resiliency, performance optimization, stakeholder collaboration, and delivery planning. Preferred experience includes CI/CD, containerization, AWS, FastAPI, dashboards, and financial markets.

Develop scalable backend services and APIs, create coding tasks and deterministic verifiers for AI systems, debug complex codebases, optimize performance, refactor legacy systems, implement features, and provide technical reviews and reasoning. The role is remote, contract-based, and part-time at 15–20 hours per week.

Leads hands-on development of agentic AI solutions on NEO, including agent execution, sandboxing, integrations, memory, retrieval, evaluations, guardrails, and authorization. Builds full-stack TypeScript and Next.js applications, writes secure production code, improves engineering automation, and supports operational stability. Mentors engineers, promotes responsible AI-assisted development, establishes validation standards, and drives adoption of emerging technologies across Software Engineering.

Leads software engineering within JPMorganChase’s Payments organization, designing, developing, testing, and troubleshooting secure, scalable production systems. The role creates architecture and design artifacts, maintains algorithms, analyzes large datasets, improves system architecture and coding practices, and uses enterprise-approved AI-assisted development tools responsibly. Required experience includes Java, Python, SQL, PostgreSQL, AWS, SDLC, agile methods, CI/CD, resiliency, and security. Databricks and machine learning frameworks are preferred.

Leads development of secure, scalable data and AI platform solutions, including batch and real-time pipelines, ETL/ELT workflows, data modeling, governance, cataloging, lineage, quality frameworks, and semantic layers. Builds Agentic Autonomous Lakehouse capabilities for governed self-service provisioning and operational optimization. Partners with technical and business stakeholders, reviews production code, improves system stability, evaluates architectures and vendors, and promotes emerging technologies across engineering communities.

Design and develop large-scale distributed systems focused on scalability, latency, efficiency, and fault tolerance. Translate product objectives into technical designs and roadmaps, own multiple systems, and identify business-impact opportunities. Provide technical oversight to engineers, participate in agile ceremonies and technical reviews, and maintain quality through code reviews. The role requires Java and J2EE development, REST APIs, Spring Boot, testing, CI/CD, databases, and related development tools.