Artificial Intelligence Jobs in Jersey City, NJ

Drive AI/ML solution sales for cable, media, and entertainment clients by developing POCs, proposing solutions, advising customers, building roadmaps, coordinating offshore delivery teams, and performing data-driven analysis to identify business improvements and implementation plans.

Lead site reliability for liquidity risk applications, owning non-functional requirements. Drive resiliency, security, scalability, monitoring, automation, and enterprise-authorized AI adoption for reliability workflows. Influence strategy, run blameless post-mortems, coach engineers, hire and develop talent, and ensure traceability, auditability, and cross-team collaboration.

Leads software engineering and quality engineering for JPMorganChase’s Payments team. Builds and maintains large-scale test automation frameworks, integrates automated testing into CI/CD pipelines, and supports application development, system design, and operational stability. Drives responsible use of AI-assisted engineering tools, improves test quality and delivery speed, and establishes validation, security, and resiliency standards. Oversees complex, high-risk projects and promotes practices such as failure triage, contract testing, service validation, and canary testing.

Leads development of secure, scalable Java and AWS software systems, including microservices, event-driven platforms, and production machine-learning systems. Designs data and feature pipelines, model deployment and monitoring, automation, CI/CD, and operational stability practices. Guides code reviews, testing, secure coding, vendor evaluations, and responsible adoption of AI-assisted engineering tools. Collaborates across product, data, platform, SRE, and governance teams while promoting MLOps, LLM, observability, and model-monitoring practices.

Lead design and implementation of Microsoft Dynamics 365 solutions, supervise and mentor teams, manage project delivery, translate complex requirements into technical designs, handle data migrations and integrations, and build strong client relationships while improving service delivery.

Supports AI strategy and GenAI adoption across Payments Sales by translating sales priorities into product requirements, technology initiatives, and actionable insights. Partners with sales, data, product, platform, CRM, and analytics teams to deliver AI-enabled use cases, improve sales lifecycle efficiency, and enhance pipeline visibility. Contributes to voice-of-sales activities, use-case prioritization, governance, stakeholder alignment, training, enablement, and monitoring of emerging AI trends.

Lead SRE for AI platforms responsible for driving reliability culture, designing resilient systems, leading incident response, mentoring engineers, establishing SLOs/error budgets, applying AI-assisted operational workflows, and improving observability, CI/CD, container orchestration, and automation across services.

Supports AI, machine learning, and generative AI product initiatives for travel products. Responsibilities include identifying opportunities, developing requirements and user stories, coordinating cross-functional delivery, tracking projects with Agile tools, analyzing product metrics, conducting user and market research, and communicating AI-driven product impacts to stakeholders.

Lead end-to-end delivery of custom technology solutions across supply chain functions (planning, manufacturing, warehouse, logistics, merchandising). Define architectures and roadmaps, identify agentic AI use cases, guide development/testing/deployment, lead cross-functional teams, manage scope/timelines/budgets, and run client workshops to translate technical solutions into business outcomes.

Define and drive multi-year fraud AI/ML platform strategy and roadmap across models, feature infrastructure, graph intelligence, and MLOps. Manage experimentation, productize graph capabilities, partner cross-functionally, and deliver measurable reductions in fraud loss while preserving customer experience.

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Develop, test, and validate Generative AI agents and data pipelines with automated testing and governance controls. Analyze complex problems, produce actionable insights, mentor junior associates, and collaborate with multidisciplinary teams to deliver AI-enabled client solutions while upholding professional standards and documentation.

Lead field-facing AI security strategy for enterprise accounts: architect AI security solutions, deliver demos/POVs, advise on LLM/RAG/agent risks, scale enablement assets, and feed product roadmap with customer feedback to securely enable enterprise AI adoption.

The Technology Consultant will deliver technology solutions for Supply Chain and Manufacturing, focusing on data strategies and digital transformation across enterprises. This role involves leading projects with modern technologies and mentoring teams for continuous professional development.

Build, test, document, launch, and support AI-powered tools, prompts, skills, agents, automations, and workflows for DC Operations. Partner with field technicians to gather requirements, maintain intake/tracking, evaluate opportunities, facilitate training, provide day-to-day support, track tool usage and impact, and convert field-built solutions into documented, supported assets.

Lead cybersecurity strategy and architecture for AI-enabled products across the CCB portfolio. Design reusable cloud-native security frameworks, perform threat modeling and secure design, guide adoption of modern security methods (A2A, API, service mesh, certificate management), and influence cross-functional teams to implement robust, scalable cybersecurity architectures.

Design, build, and optimize low-latency, high-throughput Spring Boot distributed services and cloud-native AWS architectures. Implement IaC (Terraform/CloudFormation), observability (Datadog/Dynatrace/Splunk), and containerized deployments. Own production reliability, mentor engineers, lead standards, and drive adoption of AI-assisted engineering practices and secure, scalable ML platform solutions.

Lead Infrastructure Engineer responsible for designing, deploying, and supporting secure, highly available HPE NonStop payment infrastructure. Drive workstreams, troubleshoot complex issues, integrate with other platforms, and apply AI/AIOps to improve incident triage, automate remediation, and ensure resiliency, security, and auditability.

Lead a global agile scrum team to design, build, and operate scalable, secure microservice-based applications. Integrate AI (Claude Code) into development and delivery, drive architecture and technical decisions, mentor engineers, enforce AI-assisted engineering governance, and ensure production quality, stability, and automation across the SDLC.

Lead design and delivery of production-grade ML and generative AI solutions for Markets Operations. Set technical direction, deploy and monitor models, manage a team, ensure reliability and governance, partner with stakeholders to prioritize and measure impact.

Lead an agile engineering team to design, develop, and operate secure, scalable Java services on AWS (ECS/EKS) with Postgres. Provide technical direction, adopt AI-assisted development practices, ensure operational stability, build observability dashboards (Splunk/Dynatrace/Grafana), and drive multi-region, zero-downtime deployments while promoting SDLC governance and reuse.

Lead development and delivery of secure, production-grade AI-enabled applications. Design, code, test, and operate AI features; drive adoption of AI-assisted engineering practices; evaluate model safety and integrations; automate remediation and reliability; coach teams on responsible AI, secure coding, and CI/CD for cloud-native environments.

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.

Lead firm-wide adoption and operationalization of AI/LLM-enabled software engineering across the SDLC/PDLC. Design and scale agentic systems, orchestration, guardrails, monitoring, and secure integrations. Partner with platform and App Dev leaders to deliver production-grade, auditable AI workflows that reduce developer toil, improve reliability, and meet governance, security, and observability standards.