Artificial Intelligence Jobs in Jersey City, NJ

Provides legal counsel on U.S. privacy, cybersecurity, data management, software development, artificial intelligence, and incident response. Advises cross-functional teams, assesses regulatory and business risks, supports privacy impact assessments, negotiates privacy and cybersecurity provisions, and assists with compliance and legal programs. Requires strong legal research, negotiation, communication, judgment, and knowledge of financial services operations, privacy laws, cybersecurity regulations, and emerging AI technologies.

Build predictive and cohort models supporting underwriting and marketing. Source, evaluate, combine, and analyze internal and external data; develop analytical datasets and solutions; implement and monitor models in production; communicate insights to business partners; document methodologies and results; and support statistical analysis requests. May supervise 1–2 analysts and provide guidance on cohort modeling and related analytics techniques.

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 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 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 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.

Lead product strategy and commercialization for AI-focused software within Internal Firm Services. Drive innovation, market and competitive analysis, TAM assessment, go-to-market planning, and portfolio management. Set strategic direction, maintain executive client relationships, mentor teams, and validate revenue potential to support investment and commercialization decisions.

Define and drive vision, strategy, and roadmap for AI-powered merchant products using LLMs and agentic workflows. Size commercial opportunities, develop business cases, lead pilots through production, partner with engineering and data science, embed governance and responsible AI, and track product performance and adoption.

Lead design and delivery of agentic LLM systems to translate mainframe logic into verified modern services. Build ingestion, RAG pipelines, multi-agent orchestration, evaluation/verification harnesses, runtime platforms, ETL/CDC integrations, and operate LLMOps for production reliability while partnering with domain SMEs and driving AI-assisted engineering governance.

Design, build, and operate agentic LLM systems and RAG pipelines to translate legacy mainframe logic into verified Kotlin/JVM services. Develop ingestion, orchestration, verification, testing, and LLMOps infrastructure, partner with domain SMEs, extend ETL/CDC pipelines, and drive adoption of safe, enterprise AI-assisted engineering practices to deliver reliable, auditable production services.

Lead design, build, and operate scalable, secure cloud platform capabilities. Develop Java/Spring Boot services, implement IaC with Terraform, run Kubernetes in HA, deliver CI/CD, apply SRE practices, use observability/APM tooling, and prototype AI/agentic workflows. Mentor engineers and enforce reliability, security, and compliance.

Provide end-to-end application and infrastructure support to ensure production stability and availability. Monitor environments with observability tools, triage incidents, validate AI-assisted recommendations, escalate when needed, and drive remediation and problem management across full-stack systems.

Design, build, and productionize scalable AI/ML and GenAI solutions (LLMs, agentic frameworks) using Python, PySpark and Databricks. Collaborate with cross-functional teams, develop prototypes to production, perform R&D, mentor team members, and communicate results to stakeholders to drive measurable business outcomes.

Support privacy governance and regulatory compliance across the Americas by advising business units, managing privacy risk, conducting privacy impact assessments, maintaining records of processing activities, coordinating incident and forensic responses, developing policies and training, and working with Legal and Compliance to implement privacy controls for new technologies.

Participate in a two-day hackathon solving nonprofit data problems and network with Data & AI teams. Potential pathway to a 2027 Data & AI summer internship where you would build end-to-end data, analytics, and ML solutions, design data platforms and pipelines, develop production-ready models and dashboards, ensure data governance and compliance, work on agile teams, and deliver data-driven recommendations.

Lead AI value measurement for Consumer & Community Banking by defining frameworks, aligning metrics and methodologies, ensuring data quality, and producing executive-ready reporting. Coordinate cross-functional stakeholders, translate analyses into clear recommendations, support business cases and performance measurement, and drive process improvements to increase transparency and accountability for AI investments.

Lead design, build, and deploy scalable generative AI and ML solutions from prototype to production. Define semantic modeling and ontology strategy, integrate semantics across data pipelines and APIs, enable ontology-driven agents and workflows, implement MLOps, monitoring, and responsible AI controls, and mentor engineers and data scientists to deliver measurable business outcomes.

Lead design, code, and architect scalable hybrid-cloud solutions for transactional and analytics workloads. Implement and review high-quality code, CI/CD pipelines, and AI/GenAI systems. Collaborate with development leads and stakeholders, present technical updates, prioritize work, and mentor team members to ensure robust, production-grade delivery and monitoring.

Lead cross-functional transformation programs to embed AI and automation into control management workflows. Diagnose processes, define outcomes and KPIs, build roadmaps, design people/process/technology solutions, run governance from pilot to scale, manage risks and dependencies, and measure benefits while ensuring responsible, compliant AI adoption across the firm.

Lead design and delivery of secure, scalable distributed systems and AI-enabled applications. Drive architecture, CI/CD, IaC, and operational excellence; mentor engineers; implement retrieval-augmented generation and AI-assisted engineering practices with appropriate guardrails and validation.

Lead an agile team to design, develop, and operate low-latency, high-throughput distributed backend services and APIs using Java, Spring Boot, AWS and Kafka. Drive secure, scalable production code, adopt enterprise AI-assisted engineering practices, improve operational stability through automation, and evaluate vendor and architectural solutions.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms on AWS. Design retrieval/RAG, agents, guardrails, tooling, observability, and APIs. Optimize performance, cost, and security; establish evaluation and testing frameworks; mentor engineers and drive cross-team technical standards in a regulated enterprise.

Lead Software Engineer responsible for designing, building, and maintaining scalable data pipelines and platform components (cataloging, quality, lineage, governance). Implements data modeling, partners with stakeholders to deliver production-grade code, automates operations, evaluates vendors, and leads Agentic Autonomous Lakehouse and engineering communities to drive platform innovation and operational stability.