Artificial Intelligence Jobs in Jersey City, NJ

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.

Advise Technology stakeholders on data ownership, governance, risk, and management practices. Lead maturity assessments, strategic roadmaps, operating model improvements, metrics, executive reporting, and capability enhancement initiatives. Partner with senior leaders across Technology, Risk, Controls, and CDAO to improve data quality, metadata, regulatory alignment, and governance effectiveness. Identify opportunities to apply AI, automation, and modern data platforms to scale data management outcomes.

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.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

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.

Own AI portfolio reporting, governance, and executive communications within Risk Management and Compliance. Maintain reporting logic, reconcile data across systems, analyze portfolio trends and risks, develop dashboards and scorecards, support governance forums, and improve reporting through standardization, automation, controls, and analytics. Collaborate with technology, data science, finance, and control teams to strengthen visibility into AI adoption, value, performance, and responsible growth.

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 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.

Leads complex technology projects and strategic initiatives for Asset & Wealth Management Technology. Responsibilities include analyzing operational and financial data, conducting industry research, developing quantitative and qualitative analyses, coordinating leadership meetings, preparing executive communications, managing stakeholders and risks, and supporting technology strategy execution.

Leads complex, cross-functional technology programs and strategy initiatives across Asset & Wealth Management. Responsibilities include defining technology priorities, analyzing trends and competitors, developing strategic recommendations, managing roadmaps, resources, budgets, risks, and workstreams, and advising senior executives. The role requires strong stakeholder influence, executive communication, consulting expertise, and the ability to align AI, digital transformation, cloud, analytics, and infrastructure initiatives with business objectives.

Leads consulting engagements focused on product development and manufacturing transformation. Diagnoses lifecycle bottlenecks, improves cost, quality, speed to market, lean processes, supply chain integration, and digital operations. Manages client workstreams, teams, budgets, deliverables, and risks while coaching junior staff. Develops client recommendations involving product strategy, roadmaps, positioning, and go-to-market planning, and applies analytics, AI, and emerging technologies to improve operational performance.

Supports operations consulting engagements focused on product development and manufacturing excellence. Analyzes workflows, identifies bottlenecks, develops strategy and transformation deliverables, documents operating models, interprets operational data, and supports lean manufacturing and supply chain integration. Builds client relationships, coordinates workstreams, guides junior team members, and applies product lifecycle management, AI fluency, and process improvement methods to improve speed to market, cost performance, and product quality.

Leads cybersecurity architecture strategy and solutions across cloud-based applications and technical domains. Analyzes risk, designs controls, evaluates emerging technologies, and drives automation. Develops security analytics dashboards, data models, pipelines, alerts, and executive reporting while ensuring data quality, traceability, resiliency, and auditability. Partners with vendors, technical teams, and senior business leaders to improve cybersecurity outcomes.

Leads technical account management and customer success for a firmwide data and AI platform. Owns customer engagement, onboarding, adoption, retention, delivery plans, governance, technical discovery, escalations, operational readiness, and expansion opportunities. Partners with Product, Engineering, SRE, and operations teams to translate requirements into production-ready solutions, drive cross-functional execution, improve customer health metrics, and establish scalable success practices across complex internal customer organizations.

Leads product strategy, vision, discovery, roadmap, backlog, delivery, and performance measurement for an agentic AI platform powering Chase digital self-service products. Partners with technology, analytics, and cross-functional stakeholders to deliver reliable AI capabilities, establish evaluation and quality frameworks, manage production issues, and promote responsible AI adoption in a regulated environment.

Leads third-party cyber and technology risk assurance, including strategy, governance, control assessment, remediation, regulatory alignment, reporting, and risk analytics. Partners with control owners, procurement, legal, security, and business stakeholders to strengthen third-party controls and contractual protections. Uses authorized AI to synthesize evidence and support executive reporting while validating outputs for security, auditability, and regulatory compliance. Drives continuous improvement and manages complex cross-functional initiatives.