Artificial Intelligence Jobs in Jersey City, NJ

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.

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.

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.

The Product Marketing Director will lead product marketing strategy, positioning, messaging, go-to-market execution, market research, launches, and post-launch optimization. The role serves as an Adobe technology subject matter expert and develops joint solutions with Merkle services. Responsibilities include cross-functional collaboration, KPI analysis, stakeholder engagement, competitive intelligence, and reporting to senior leadership. The position requires translating complex technical capabilities into customer benefits and driving product awareness, adoption, and growth.

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.

Leads hands-on Java and AI engineering for CitiDirect, integrating AI practices with production software development. Responsibilities include mentoring developers, conducting code reviews, establishing DevOps and CI/CD practices, designing automated testing frameworks, building microservices and scalable solutions, and promoting Docker, Kubernetes, and other development tools. The role partners with architecture and testing teams, drives technology adoption, improves engineering productivity, and supports responsible AI, performance optimization, and knowledge sharing.

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.

Lead architecture and delivery of production AI/ML solutions for wholesale payments, building NLP/document-AI and LLM-enabled applications on AWS, establishing MLOps and model governance, measuring business outcomes, and mentoring engineers.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Lead and mentor a team to design, build, and operate agentic LLM systems and production LLM-backed APIs. Own end-to-end ML lifecycle, MLOps, inference optimization, vector memory, safety/guardrails, and cross-functional governance for scalable, secure AI deployments.

Lead the AI use case portfolio for Trade and Working Capital, translating business needs into technical requirements, prioritizing and managing delivery from ideation to value realization, enabling product, commercialization, and productivity AI use cases while driving adoption and governance alignment across stakeholders.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Lead design, delivery, and production deployment of enterprise AI/ML solutions for wholesale payments operations. Build scalable NLP, document understanding, LLM-enabled applications on AWS, establish MLOps, model governance, monitoring, and responsible AI. Mentor engineers, set engineering standards, and partner with stakeholders to define roadmaps and measurable outcomes.

Lead design and delivery of reliability, observability, and SRE practices for large-scale data and AI/ML platforms. Define NFRs, SLI/SLOs, incident response, and implement reliable, secure, scalable infrastructure and automation while mentoring teams and driving safe, auditable AI-assisted operations.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.