Artificial Intelligence Jobs in Jersey City, NJ

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.

Lead design, build, and operate production-grade ML and generative AI systems. Drive architecture and MLOps, fine-tune LLMs for NLP use cases, implement monitoring, mentor engineers, partner with product and data science, and communicate results to technical and non-technical stakeholders.

Lead design, build, and deploy production-scale generative AI and ML solutions. Define semantic modeling strategy and unified semantic layer, create ontologies, integrate semantics across pipelines and APIs, enable ontology-driven intelligent workflows, and implement model training, monitoring, governance, and responsible AI practices while partnering with stakeholders.

Lead design, implementation, tuning, testing, and support of highly available IBM Power/AIX platforms. Drive integration testing, POCs, automation (Ansible, Python, shell), observability, and AI-assisted infrastructure workflows. Mentor engineers, collaborate cross-functionally, ensure security/compliance, and improve resilience and operational excellence.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Lead end-to-end AI model development and deployment, design and operationalize multi-agent systems with HITL controls, implement drift monitoring and retraining, define evaluation frameworks, embed AI into workflows using cloud/data mesh/knowledge bases, integrate analytics into operations, and communicate findings to senior leadership.

Lead architecture and delivery of production-grade agentic AI and NLP solutions for client onboarding. Design reusable frameworks (RAG, multi-agent orchestration, LLMs), integrate agents into enterprise systems, define safety/guardrails, advise stakeholders, and champion AI-assisted SDLC improvements to improve delivery, quality, and operational outcomes.

Lead execution of a firmwide data product approach: define, develop, register, and govern reusable data products. Coordinate with business and CDAO teams, set KPIs/OKRs, produce strategic reports, advise data leaders, and drive adoption of best practices to enable analytics and AI use across the firm.

Lead strategy and execution for C360 Data Science Center of Excellence, driving AI/ML and modeling initiatives. Collaborate with Risk, Finance, Technology, and CDAO; manage global analytical teams; create executive-level presentations; align stakeholders and advance data-driven solutions including LLMs, semantic search and RAG for financial forecasting and insights.

Lead an agile engineering team to architect, develop, and productionize secure, scalable software that integrates LLMs and generative AI. Drive AI-assisted engineering practices, mentor engineers, translate business requirements into technical roadmaps, automate reliability, and ensure compliant, high-quality delivery.

Lead the design and implementation of AI-enhanced, pixel-perfect React front-end interfaces for large-scale internal tools. Own and maintain code quality, performance, accessibility (WCAG), testing, and CI/CD practices. Collaborate with product, design, and backend teams, mentor junior engineers, and drive responsible adoption of enterprise-authorized AI-assisted development tools.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead design, develop, and maintain secure, scalable data and AI-driven systems. Build data pipelines on AWS/Databricks, write production code, drive AI-assisted engineering adoption, enforce validation/security standards, and mentor teams on responsible AI practices.

The AI Executive Partner will advise C-Suite executives on leveraging AI to drive business outcomes, manage member relationships, and deliver innovative AI solutions.

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.

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 strategic success and adoption for a portfolio of higher-education and government Workday customers. Build C-level relationships, drive deployment and post-production adoption, position upsell opportunities, create Success Plans, resolve escalations, and coordinate internal and partner teams to ensure customer satisfaction, subscription retention, and revenue growth. Advocate product value, guide complex multi-institution deployments, and enable customer self-sufficiency.

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.

Supports Relativity Success leadership through strategic planning, prioritization, executive reporting, and business operations. Builds cross-functional relationships, gathers operational intelligence, identifies dependencies and risks, recommends actions, and contributes to annual planning. The role combines analytical rigor, relationship management, process development, and decision support in a SaaS environment.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.