Machine Learning Jobs in Jersey City, NJ

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 deploy data solutions using Palantir Foundry and AIP for client engagements. Lead technical teams, coach team members, analyze system interactions, build applications with large datasets, and apply data engineering and analytics to deliver client outcomes. The role also includes managing client relationships, planning and executing projects, encouraging technology adoption, resolving stakeholder conflicts, and driving continuous improvement.

Leads the development and deployment of data solutions using Palantir Foundry and AIP. Manages engineering teams, coaches team members, oversees project planning and execution, analyzes system interactions, manages client relationships, and drives innovation. The role applies software engineering, data engineering, analytics, and AI/ML techniques to deliver scalable client solutions while maintaining technical and professional standards.

Leads the development and deployment of software and data solutions using Palantir Foundry and AI/ML technologies. Responsibilities include data engineering, analytics, system improvement, client account management, project ownership, team leadership, coaching, stakeholder engagement, and continuous improvement. The role requires building scalable applications with large datasets and delivering customer-focused solutions.

Leads enterprise data and AI governance across policies, controls, risk management, compliance, privacy, cybersecurity, model risk, data quality, access management, and responsible AI. Partners with technology, legal, audit, risk, and business teams; oversees regulatory examinations and control remediation; presents governance metrics to executives; and builds a high-performing governance and AI policy team.

Leads technology risk and control activities across an application portfolio. Responsibilities include identifying and mitigating operational and compliance risks, evaluating control effectiveness, managing issues, conducting root cause analysis, reporting risk insights, and supporting regulatory compliance. The role also applies enterprise AI and machine learning governance, validates AI-assisted outputs, protects sensitive data, and advises technology leaders and senior management on risk posture and remediation strategies.

Develop AI/ML proofs of concept and deployable models for compliance, conduct, and operational risk analytics. Analyze structured and unstructured data, build scalable data pipelines, formulate quantitative methodologies, and create actionable analytical solutions. Collaborate with data scientists, technology partners, risk professionals, and model validation teams. Document models and quantitative analyses for governance and model risk review, while developing LLM, agentic AI, graph learning, and statistical modeling solutions.

Lead the vision, strategy, and delivery of enterprise data products across Consumer & Community Banking. Drive continuous discovery, empowered product teams, AI-powered self-service capabilities, data governance, adoption, and measurable business outcomes. Partner with engineering, design, and leaders across multiple business lines to prioritize opportunities, manage product risk, and ensure privacy, reliability, and compliance in a regulated environment.

Leads end-to-end generative and agentic AI initiatives, from research and experimentation through production deployment. Designs foundation-model solutions, evaluation frameworks, semantic modeling standards, and responsible AI controls. Partners with product, engineering, risk, and business stakeholders to define outcomes, optimize reliability and performance, and deliver scalable AI services. Mentors engineers and data scientists while communicating technical recommendations to diverse audiences.

Leads financial statement audits for digital assets and financial services clients, evaluating regulatory compliance, governance, risk management, and internal controls. Applies advanced auditing and technical accounting expertise to identify material misstatements and issue audit reports. Directs complex engagements, coaches diverse teams, manages client relationships, communicates findings, and reinforces professional, independence, and ethical standards. The role also involves data analysis and exploring AI and machine learning platforms to improve processes.

Design, build, deploy, and operate production LLM-powered agents on JPMorganChase’s NEO platform. Responsibilities include Graph RAG, agent memory, context and entitlement management, multi-agent workflows, MCP integrations, evaluations, guardrails, observability, cloud deployment, security, resiliency, and collaboration with business and engineering stakeholders. The role also requires identifying data and architecture improvements across the software development lifecycle.

Leads ITSM Problem Management for production technology services, driving root cause analysis, incident recurrence prevention, service resilience, operational readiness, and process improvements. Collaborates with engineering, development, and support teams; manages ServiceNow problem tickets and defects; supports audits and regulatory compliance; and presents operational, risk, and service metrics to senior stakeholders. Uses Python, AI, and data analysis to automate problem management and improve decision-making.

Lead the development and production deployment of generative AI, agentic AI, and machine learning solutions in Python. Establish enterprise semantic modeling, ontology, and unified semantic layer standards; build AI pipelines, evaluation frameworks, intelligent workflows, and MLOps capabilities. Implement responsible AI governance for regulated environments, partner with business and technical stakeholders, and mentor engineers and data scientists while influencing enterprise technical direction.

Leads the end-to-end lifecycle of data and AI governance products, from discovery and roadmap development through delivery, adoption, and value measurement. Owns prioritization, backlog management, stakeholder alignment, governance-by-design controls, auditability, documentation, and risk reduction. Partners with product, engineering, data science, legal, and risk teams to automate and standardize governance processes, communicate complex topics to senior leaders, and manage delivery health across concurrent initiatives.

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 AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads the strategy, roadmap, development, and performance of risk tooling products supporting control assurance, audit readiness, risk reporting, and operational risk management. Drives automation of manual risk and controls processes, partners with Engineering, Risk, Audit, Controls, and Architecture teams, establishes governance for complex initiatives, and advances enterprise analytics and AI-enabled risk solutions. Coaches product teams, manages stakeholders, monitors market trends, and ensures product investments deliver measurable business outcomes.

Lead the development of data, analytics, automation, and AI-enabled decision-support capabilities for infrastructure portfolio management and strategic execution. Build dashboards, reusable analytics patterns, predictive models, generative AI solutions, and reporting standards that improve investment governance, risk identification, and execution outcomes. Partner with technology, product, risk, and controls stakeholders to ensure responsible AI use, strong data lineage, human oversight, and scalable insights for senior executives.

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.

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.

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.

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.

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.