Artificial Intelligence Jobs in Jersey City, NJ

Lead frontend engineering using Angular across the SDLC, drive architecture and delivery, embed AI-assisted development practices, ensure quality, performance, security, and mentor the team while collaborating with Product and QA.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

The Tech Lead will design and develop Python-based backend services for NLP and chatbots, support cloud-native technology transformation, and collaborate with stakeholders.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

Lead and coordinate technical AI and digital transformation projects across data, model, and platform domains. Translate architecture into project plans, manage cross-functional dependencies, enforce CI/CD and observability, oversee testing and compliance (regulatory and security), create runbooks and incident response, and ensure audit-ready evidence and controlled production cutovers.

Hunter-oriented BDM responsible for sourcing and qualifying new enterprise and mid-market opportunities across North America, building pipelines, outbound prospecting, coordinating discovery, partnering with leadership to close deals, and attending events (travel ~40%). Focus on selling consulting and technology services.

The Senior Software Engineer will lead the development of AI solutions for the life insurance sector, overseeing full stack application design and delivery while guiding junior engineers.

Lead AI and data transformation initiatives, develop AI strategies, deliver AI/ML solutions, mentor teams, and drive innovation for enterprise clients.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

The AI Engineer will build AI-powered applications, prototype solutions, implement workflows using LLMs, and manage sub-projects while improving internal tools.

The AI Platform Architect will lead the design and optimization of the EisnerAI Platform on Azure, ensuring performance, reliability, and cost efficiency, while mentoring teams across the organization.

As a Business Process Automation Engineer, you'll design low-code solutions using Microsoft Power Platform and AWS tools, enhance workflows with AI, debug technical issues, and collaborate with stakeholders.

The AI Product Owner will lead cross-functional AI projects, transforming internal processes and driving measurable outcomes by leveraging AI solutions across various business functions.

Lead development and execution of enGen and enterprise AI strategy, drive AI adoption and commercialization, manage and grow teams, ensure operational excellence and compliance, partner across the enterprise to deliver scalable cloud-based AI solutions, and generate new revenue streams through AI initiatives.

Lead AI-powered product innovation for legal enterprise solutions, prototyping and validating GenAI/ML workflows (LLMs, RAG, NLP). Drive validation and tuning of LegalVIEW BillAnalyzer, collaborate with domain experts, engineering, and sales for go-to-market support, evaluate model outputs, define success metrics, and ensure compliance, data residency, and responsible AI adoption.

Lead development and scaling of a multi-tenant agentic AI platform, writing production code and guiding architecture. Build agent frameworks, RAG and vector/graph integrations, fine-tuning pipelines, and collaborate with SMEs and frontend teams to deploy agent-driven enterprise workflows.

Support the development of security monitoring and automated response capabilities. Analyze security events, assist in building workflows, and collaborate with various teams to improve detection and automation.

Lead design and deployment of agentic AI and GenAI solutions to automate production runtime operations. Build and productionize LLMs, retrieval and RAG pipelines, integrate agents with observability and incident systems, enforce safety/governance, optimize cost/performance, and mentor engineering teams to deliver auditable, reliable, and scalable ML-driven operational outcomes.

Lead end-to-end delivery of Voice AI projects: scope, plan, track, and hand off production. Align product to customer needs, set and monitor metrics, manage scope, resources, budgets, testing, and client communications while working closely with engineering and data teams.

Lead design and implementation of the EisnerAI platform, integrating data, governance, AI agents and applications. Drive model evaluation, post-training optimization, agent development (TaxPrepBot, DocIntake, etc.), and ensure data governance and responsible AI practices across enterprise solutions.

The AI Product Manager oversees the strategy, roadmap, and execution of AI solutions, collaborating with teams to address operational challenges and ensure product safety and effectiveness while monitoring performance.

Build and ship AI-powered automations and agents across business teams. Contribute to OUTGPT platform, implement integrations (APIs, SQL, Snowflake, Salesforce), deploy to AWS, and collaborate with stakeholders while receiving and giving mentorship.

Own and grow OUTFRONT's internal AI platform and external agent infrastructure: move OUTGPT to production, evolve the Agency Connect MCP, design agent infrastructure (routing, guardrails, observability, secrets, cost controls), operationalize AWS AI stack, integrate with internal systems, and mentor the AI & Automation engineering pod.

The AI Engineer will analyze business processes for automation opportunities, design and maintain RPA solutions, and develop Intelligent Document Processing systems while collaborating with stakeholders and ensuring reliability of solutions.

The Principal Data Scientist will create scalable data solutions and machine learning models, collaborating with teams to drive innovation in the financial industry.