Artificial Intelligence Jobs in Jersey City, NJ

Leads enterprise email security strategy, architecture, engineering, modernization, and lifecycle management. Oversees phishing protection, threat detection, encryption, DLP, cloud security, incident response, risk remediation, governance, metrics, vendor relationships, and regulatory alignment. Partners with cross-functional stakeholders, presents to executives, leads engineering teams, and applies AI-enabled workflows to improve security analysis and operational effectiveness.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads Microsoft Dynamics 365 CRM and Copilot/AI functional workstreams for client engagements. Responsibilities include requirements analysis, CRM solution design, implementation management, integration planning, configuration, deployment, issue resolution, budgeting, risk tracking, user training, adoption support, and stakeholder management. The role also coaches team members, reviews deliverables, and identifies AI-enabled improvements to operational efficiency and client outcomes.

Lead client-facing Microsoft Dynamics 365 ERP and AI/Copilot solution architecture programs. Translate business requirements into functional designs, implementation plans, configurations, testing, training, and deployment activities. Direct complex workstreams, improve processes and controls, resolve risks, communicate recommendations to stakeholders, build client relationships, and coach consulting teams through ambiguity and change.

Lead control management for Treasury and Chief Investment Office resolution and recovery planning. Design, test, monitor, and evidence controls; assess operational risks; identify and remediate issues; perform root-cause and lessons-learned analyses; support regulatory reviews; and coordinate with business, compliance, audit, and control stakeholders. Maintain risk and control inventories, drive governance and escalation, and promote consistent control practices across regions while supporting regulatory readiness and sustainable risk mitigation.

Leads a team of syndicated loan coordinators overseeing corporate syndicated loan execution, transaction closings, escalations, operational controls, regulatory adherence, audits, and stakeholder relationships. Serves as the operational subject matter expert for complex loan transactions, represents Wholesale Lending Services in business and risk forums, and drives projects, working groups, automation, and process improvements.

Serve as a senior Second Line of Defense cybersecurity risk partner, independently assessing cybersecurity controls, secure development practices, cryptographic strategies, AI governance, and technology risks. Provide risk opinions, challenge risk acceptance decisions, recommend remediation, and enhance cybersecurity frameworks, standards, policies, and governance. Engage with technical and executive stakeholders, monitor emerging threats and regulatory expectations, and support research and testing across a complex financial services environment.

Owns the operating model for an internal product organization, improving intake, planning, documentation, communication, workflows, and stakeholder enablement. Partners with product, design, engineering, and business teams to reduce operational friction, build scalable tools and automations, measure adoption, and apply AI-enabled workflows to improve team effectiveness.

Develops and governs architecture solutions for complex software applications and platforms within Employee Platforms. Responsibilities include system design, secure coding, technical troubleshooting, technology evaluation, architectural governance, AI-enabled engineering patterns, and maintaining application resiliency, security, auditability, and operational stability.

Leads technology resiliency governance across an asset and wealth management application portfolio. Oversees disaster recovery, cyber recovery, data restore, and sustained operations testing; maintains documentation, evidence, taxonomy alignment, and control compliance. Produces audit-readiness reporting, identifies emerging risks, coordinates remediation, and advises application owners. Also supports production incident, problem, and change management, observability, service restoration, and responsible adoption of enterprise AI for operational workflows.

Own technology portfolio planning, investment governance, executive reporting, data quality, risk controls, audit readiness, and process improvement. Partner with Engineering, Product, Finance, and business stakeholders to manage priorities, budgets, dependencies, and delivery health. Lead adoption and governance of AI-enabled and agentic workflows with human validation, traceability, security, and operational risk controls.

Owns the Trade and Working Capital AI use-case portfolio from ideation through delivery and value realization. Translates business needs into technical requirements, prioritizes opportunities, coordinates with AI, data science, technology, data, and infrastructure teams, and drives adoption across product, commercial, and productivity applications. The role also leads AI literacy initiatives, aligns business priorities with enterprise governance and AI programs, manages multi-workstream delivery, and reports progress and realized value to senior stakeholders.

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.

Leads complex, cross-functional technology initiatives from planning through delivery. Manages milestones, dependencies, risks, change control, stakeholder alignment, executive reporting, documentation, production readiness, and continuous improvement. Partners with product and engineering teams on modernization and enterprise technology programs while using AI-assisted workflows with appropriate validation and data-sensitivity controls.

Advise clients on privacy, data protection, and responsible AI requirements. Evaluate AI systems for fairness, transparency, safety, privacy, accountability, and third-party risk. Develop governance operating models, policies, risk-tiering approaches, controls, impact assessments, and client deliverables. Interpret regulatory changes, support breach investigations, collaborate with legal, compliance, cybersecurity, data, and technology teams, and mentor junior staff. Apply technical literacy in AI/ML, GenAI, software development, technical architecture, and Python.

Defines conceptual, logical, and physical data architectures for wealth management data products. Owns data models, taxonomies, metadata, semantic layers, canonical concepts, governance standards, and schema-evolution patterns. Partners with business and engineering stakeholders to deliver scalable, analytics- and AI-ready data solutions across warehouses, lakehouses, APIs, and BI platforms. Applies automation to improve metadata generation and documentation while ensuring controls, versioning, interoperability, privacy, and sustainable architecture evolution.

Lead development of wholesale credit risk models supporting CECL, CCAR, client ratings, and regulatory risk management. Build scalable quantitative methods, high-performance computing solutions, analytics frameworks, and modeling pipelines using large datasets and machine learning. Assess model performance and risk, document methodologies, and communicate results and limitations to credit, finance, technology, governance, and regulatory stakeholders.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads the design, experimentation, evaluation, and productionization of machine learning, deep learning, NLP, LLM, and generative AI solutions. Develops production-ready Python code, optimizes model performance, integrates generative AI into ML platforms, and partners with product and engineering teams. Provides technical leadership, mentors practitioners, and drives scalable, measurable solutions using advanced techniques such as fine-tuning, embeddings, RAG, prompt engineering, and distributed training.

Leads the strategy, architecture, and production scaling of agentic AI capabilities across a Databricks-based data platform on AWS. Oversees AI-enabled data modeling, pipeline generation, classification, governance, tool integration, monitoring, security, and human-in-the-loop controls. Manages cross-functional delivery, technical roadmaps, stakeholder communication, and the hiring and development of AI and data engineering teams. Establishes responsible AI, SDLC automation, resiliency, auditability, and quality standards for enterprise adoption.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Leads AI controls and data governance strategy across client onboarding, documentation operations, and CDAO functions. Oversees AI risk assessments, data quality and lineage controls, model monitoring, advanced analytics, automated testing, remediation, regulatory alignment, and executive advisory activities. Builds global teams and promotes responsible AI practices using Python, GenAI, and LLM automation to strengthen control effectiveness and risk intelligence.

Leads complex technology projects and programs from planning through delivery. Responsibilities include defining scope, allocating resources, managing timelines and risks, coordinating software development, QA, and business stakeholders, monitoring progress, communicating status, and ensuring governance. The role also uses enterprise-authorized AI to accelerate planning and reporting while validating outputs and protecting sensitive data.

Advises pharmaceutical and life sciences clients on R&D operations, clinical trial design, organizational design, process optimization, regulatory compliance, analytics, and portfolio management. Develops actionable strategies, executive presentations, and decision-support insights while coordinating projects and client workshops. Guides junior team members, supports change management, and helps clients improve R&D productivity, operational effectiveness, growth, and innovation across the drug development lifecycle.