Artificial Intelligence Jobs in Jersey City, NJ

Lead delivery of complex technology programs for Asset & Wealth Management by defining strategy, scoping problems, advising senior stakeholders, managing cross-functional teams, driving roadmaps, and delivering executive-level communications while mentoring junior staff and managing risks and resources.

Lead firmwide data management and governance strategy to make data AI-ready. Define vision, prioritize initiatives (data products, metadata, publishing), perform analysis and industry research, create executive presentations, influence senior stakeholders, and coach junior team members.

Lead cybersecurity architecture for enterprise applications and platforms: evaluate principles and controls, design cloud-native secure solutions, provide technical guidance, influence stakeholders, apply threat modeling and frameworks, and build reusable security patterns and guardrails to improve security outcomes.

Lead a software engineering team to design, build, and maintain secure, resilient Java-based applications. Develop and review production-quality code, drive adoption of AI-assisted engineering practices, automate remediation and SDLC tooling, evaluate vendor solutions, and coach engineers on secure and responsible AI use. Promote reuse, operational stability, and agile/CI-CD practices within financial services applications and cloud-native environments.

Design, develop, test, and maintain secure, high-quality production software. Create architecture and design artifacts, analyze large datasets to improve systems, troubleshoot complex problems, and contribute to engineering communities and inclusive team culture.

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.

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.

Lead development of agentic-AI capabilities and an evaluation/provenance harness to safely automate bulk code changes. Design CI/CD integrations, PR-provenance controls, observability, test/eval tooling, rollout of standards, and operational support to scale machine-authored changes while ensuring auditability, security, and reliability.

Lead design, build, and operate cloud-native AI/ML platform infrastructure and shared services. Architect scalable, secure GPU-enabled training and inference environments, implement IaC and automation, support model deployment/serving pipelines, drive observability and CI/CD, and promote responsible AI practices and enterprise AI-assisted development across teams.

Summer Analyst in Data & AI will design and build end-to-end data, analytics, and ML/AI solutions using cloud and modern tooling. Responsibilities include developing models and pipelines, running experiments tied to business KPIs, supporting data governance and compliance, creating dashboards, and collaborating in agile teams while receiving mentorship and training.

Lead design and implementation of agent-driven CI/CD remediation and provenance systems. Build evaluation harnesses, integrate AI remediation into pipelines, ensure signed PR provenance, observability, and operational readiness. Measure adoption and quality metrics, support standards rollout, and coach teams on responsible AI usage in engineering workflows.

Lead ZAIDYN's AI Engineering function to design, build, and ship an enterprise-grade agentic AI platform. Own architecture, integrations (AWS Bedrock/AgentCore), LLMOps/eval frameworks, observability, governance, and team hiring/operations. Ensure production readiness, cost/latency controls, auditability, and cross-functional delivery with product and QA while representing the platform externally.

Lead full-stack development of dashboards and backend services, build ETL/data pipelines on Databricks and Snowflake, and drive safe, governed LLM/agentic development across the SDLC. Provide architecture, stakeholder leadership, AI-assisted engineering best practices, and ensure reliability, reproducibility, and compliance in treasury, risk, and PnL systems.

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.

Lead development of secure, high-quality production software for analytics and credit risk; design, code (Python/Java), review, troubleshoot, automate remediation, drive CI/CD and cloud-native solutions, mentor junior engineers, evaluate vendors, and promote new technologies.

Lead technical delivery for analytics-focused software: design, develop, test, and operate secure, high-quality production code. Drive automation and CI/CD, evaluate vendor/architecture choices, mentor junior engineers, and promote resilient cloud-native solutions within financial services technology teams.

Lead firmwide technology strategy for Data & AI, build partnerships with cloud and infrastructure vendors and the venture ecosystem, drive innovation adoption, evaluate and scale new technologies, raise internal awareness of Data & AI opportunities, support client-facing business development and strategic investments, and influence senior stakeholders to align partner roadmaps with JPMC objectives.

Lead transformation of strategic plans into programs and projects across business units. Oversee execution, risk mitigation, and data-driven progress monitoring. Build and lead teams, engage stakeholders, resolve conflicts, and ensure alignment with business strategy.

Support product strategy and roadmap for Merchant Services. Analyze product performance metrics, contribute to solution discovery, participate in planning, define user workflows and requirements, and collaborate with cross-functional teams to enable timely product launches.

Lead full-stack and AI-enabled software engineering for a treasury data platform. Build React/TypeScript UIs and Python backends, deliver ETL/data pipelines on Databricks/Snowflake, and establish governed LLM/agentic development practices to ensure reliability, reproducibility, and secure AI-assisted engineering across the team.

Lead design, build, and scale of generative and agentic AI solutions; deliver production ML systems, reusable platforms, observability and MLOps, mentor AI teams, partner with stakeholders to prioritize and measure enterprise impact.

Lead design, development, and production deployment of advanced AI/GenAI and LLM solutions. Build and tune models, integrate generative AI into ML platforms, drive MLOps adoption, ensure responsible AI governance, collaborate cross-functionally, and mentor AI engineers.

Lead design, development, and production deployment of AI, GenAI, and LLM solutions. Build and tune models in Python, collaborate across product and engineering, drive MLOps adoption, ensure model governance and regulatory compliance, and communicate results to technical and business stakeholders.

Lead and remain hands-on building an AI-first developer portal and secure external API platform. Define architecture for agent-ready workflows, authentication/credential flows, contract-first APIs, resiliency, observability, and AI-enabled support. Drive cross-functional delivery, coach engineers, and ensure operational controls, auditability, and production readiness for integrations.

Design, implement, and operate production-grade agentic AI and LLM-enabled services on AWS. Build retrieval-augmented systems, APIs, and integrations; optimize performance, scalability, cost, and observability; implement evaluation, testing, guardrails, and secure development practices while collaborating across product and engineering teams.