Software Engineer Jobs in Jersey City, NJ

Develop and maintain secure, scalable software for wealth management platforms. Design distributed systems, write and review Java code, troubleshoot production issues, improve application resiliency, and automate recurring remediation. Lead adoption of approved AI-assisted engineering practices with strong validation, security, and responsible-use standards. Collaborate with business stakeholders, vendors, startups, and internal teams on architectural evaluations. Applications use AWS, Spring Boot, Kafka, agile methodologies, and CI/CD.

Leads the design, development, and maintenance of scalable machine learning platforms and infrastructure. Builds tools for model training, deployment, monitoring, and lifecycle management; integrates data engineering and model-serving capabilities; develops secure APIs and automation pipelines; and improves reliability, scalability, and performance. Partners with data scientists, ML engineers, and product teams while driving MLOps, CI/CD, infrastructure automation, and AI-assisted engineering practices.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Lead design and implementation of agent-driven CI/CD remediation and provenance systems. Build evaluation harnesses, integrate AI remediation into pipelines, ensure signed PR provenance, observability, and operational readiness. Measure adoption and quality metrics, support standards rollout, and coach teams on responsible AI usage in engineering workflows.

Lead the technical direction and hands-on development of NetBox Delivery, including Kubernetes operators, controllers, APIs, release tooling, observability, and deployment infrastructure. Improve NetBox reliability and performance, establish engineering standards, mentor engineers, coordinate cross-team architecture and release initiatives, and serve as a technical escalation point during incidents.

Leads architecture and development of scalable, high-availability software systems supporting millions of endpoints. Owns complex multi-team initiatives, makes technical decisions, mentors engineers, establishes engineering standards, and improves software quality. Requires deep Java development, distributed systems, API design, database engineering, system scalability, testing, and AI-assisted development experience.

Leads development of scalable, secure, cloud-native software solutions using Golang, public cloud, and Infrastructure as Code. Provides technical guidance, reviews production code, drives AI-assisted engineering practices, supports cloud migrations, and influences architecture, application functionality, and technical operations. Acts as a subject matter expert and promotes engineering standards, automation, resilience, security, and responsible AI usage across teams.

Develop and maintain enterprise software for trade management, credit derivatives, risk calculations, regulatory reporting, and front-to-back office integration. Translate business requirements into technical designs, build Python and Java solutions, conduct comprehensive testing, and ensure compliance and reliable data flow. Collaborate with trading, finance, and market risk teams while implementing authentication, middleware, CI/CD, and distributed application architectures.

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.

Develops secure, scalable enterprise software for Whole Lending Technology. Responsibilities include designing and coding Java/Spring microservices, creating architecture artifacts, troubleshooting systems, working with AWS and relational databases, maintaining production stability, and using AI-assisted development tools responsibly. The role also involves analyzing data, improving coding practices and system architecture, collaborating in agile teams, and applying testing, CI/CD, and software development lifecycle practices.

Lead the design, development, and operation of a business observability platform using React, Java, Spring Boot, Kafka, AWS, MySQL, and Databricks. Responsibilities include building event-driven microservices, data-heavy user interfaces, CI/CD pipelines, cloud deployments, production observability, and resilient data access patterns. The role also establishes engineering standards, leads cross-functional delivery, and promotes secure, responsible use of AI-assisted development tools.

Leads the design, development, testing, and operation of secure, scalable cloud-native software services. Provides technical direction, writes and reviews production code, develops AWS APIs and event-driven microservices, manages Terraform infrastructure, and improves CI/CD, testing, observability, and reliability. Establishes governance for responsible AI-assisted engineering, validates AI outputs for security and quality, promotes engineering standards, and coaches teams on compliant development practices.

Lead Software Engineer responsible for designing, coding, testing, and operating secure, scalable 24x7 enterprise services. Provides technical leadership, reviews code, guides AI-assisted engineering practices, establishes validation and security standards, improves SDLC automation, and influences product and technical decisions. Requires proficiency in Rust, Java, Go, or C++, strong system design experience, and expertise operating tier-1 online services. Familiarity with Apache DataFusion, DuckDB, and Apache Arrow Flight SQL is preferred.

Leads architecture and software engineering for cloud-based applications within JPMorgan Chase’s CTO-IAM organization. Defines target-state architecture, evaluates emerging technologies and vendors, develops and reviews secure production code, troubleshoots systems, and improves operational stability. Establishes reusable AI-enabled engineering patterns and governance across SDLC and CI/CD practices while ensuring resiliency, security, traceability, and auditability.

Design, build, deploy, and operate production LLM-powered agents for JPMorganChase’s Commercial and Investment Bank. Responsibilities include Graph RAG, agent memory, context and authorization management, multi-agent workflows, MCP integrations, evaluations, guardrails, cloud deployment, observability, resiliency, and security. The role partners with business, product, engineering, and external stakeholders to deliver supported AI solutions.

Ten-week software engineering internship involving Java development, application design, testing, debugging, code reviews, defect resolution, and software lifecycle activities. The intern will collaborate with engineers and product managers, contribute to scalable software solutions, learn engineering best practices, and participate in Agile planning and technical discussions.

Leads two scrum teams while remaining hands-on in software development at least 50% of the time. Designs, codes, tests, debugs, and supports secure, resilient, high-throughput distributed payment systems using Java and Spring Boot. Drives AI-assisted engineering practices, automation, CI/CD, code quality, operational stability, and responsible AI usage. Evaluates technical solutions with vendors and internal teams and leads engineering communities of practice.

Develop cross-platform C++ and Qt software for network device discovery, monitoring, and data collection across Windows and Linux. Build high-performance, concurrent, asynchronous systems that communicate with thousands of devices using SNMP, SSH, ICMP, WMI, and related protocols. Own testing, troubleshoot issues across application, operating-system, and network layers, optimize reliability and performance, and collaborate with backend, UI, QA, and product teams.

Designs, develops, tests, and maintains secure, scalable distributed software for access governance. Responsibilities include decomposing technical problems, delivering high-quality production code, designing distributed and event-driven services, supporting operational stability, evolving legacy systems, and collaborating within an agile team. The role also involves applying domain-driven design, refactoring techniques, security practices, and emerging technologies across vendor, open-source, and in-house platforms.

Leads software engineering for secure, scalable technology products within data technology. Responsibilities include designing and developing Java, Python, or Go applications; building AWS-hosted APIs, microservices, event-driven services, and data models; developing Terraform infrastructure; reviewing code; improving testing and operational stability; and guiding responsible AI-assisted engineering practices, secure coding, architecture, and SDLC adoption.

Design, build, and deliver production AI/ML solutions using agile practices. Translate business requirements into ML specifications, produce secure high-quality code, create architecture artifacts, analyze large datasets, troubleshoot complex issues, and contribute to engineering communities and continuous improvement.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Leads enterprise AI Operations strategy and production lifecycle management for AI/ML applications, models, data pipelines, and platforms. Establishes deployment, governance, monitoring, observability, automation, resiliency, security, and incident-response standards in a regulated environment. Partners across engineering, infrastructure, data, risk, and production teams; influences architecture and operational controls; drives reliability improvements and continuous learning; and mentors senior technical teams in AI Ops, SRE, and production support practices.

Leads software engineering initiatives focused on scalable ETL pipelines, data lake and warehouse architecture, cloud services, observability, automation, resiliency, and operational stability. Designs and optimizes Databricks and AWS data solutions, monitors performance and costs, mentors engineers, and drives technical standards. Preferred experience includes investment banking systems, Spring Boot microservices, containerization, Kafka, and financial products.

Build and operate full-stack applications and AI-facing features for CoreWeave’s internal data platform. Responsibilities include developing TypeScript, React, Next.js, and Python services; designing APIs and relational data models; integrating analytical data platforms; deploying on Kubernetes; and owning production reliability, testing, observability, and on-call support. The role also involves collaborating with non-engineers to define processes, shipping LLM-backed applications, and evaluating AI output quality.