Machine Learning Jobs in Jersey City, NJ

Lead presales strategy for Workday Adaptive Planning with large enterprise customers. Conduct executive discovery, design tailored solution strategies, deliver demonstrations, communicate value, support sales cycles, showcase AI-enabled planning capabilities, respond to RFPs, represent Workday at events, and mentor peers. The role focuses on FP&A, workforce and operational planning, forecasting, analytics, and executive engagement, with travel of up to 50%.

Designs and governs secure, scalable enterprise AI architectures across generative AI, machine learning, automation, and agent-based solutions. Establishes reusable patterns, standards, guardrails, and governance while integrating AI with enterprise data, applications, APIs, and cloud platforms. Advises teams on responsible AI, MLOps, LLMOps, monitoring, resilience, cost, compliance, and lifecycle management. Evaluates emerging technologies, supports proofs of concept, oversees architecture across initiatives, and communicates technical decisions to business and technology stakeholders.

Lead technical strategy for financial services customers by designing scalable data, AI, streaming, and analytics architectures. Own customer engagements from discovery through production deployment, advise senior technical stakeholders, drive Databricks adoption, lead competitive technical wins, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, distributed data systems expertise, public cloud deployment experience, and the ability to translate complex architectures into business value.

Lead technical onboarding and enablement for contact center partners, architecting scalable Zendesk, Amazon Connect, and AWS solutions. Provide co-selling support, technical strategy, solution design, demonstrations, proof-of-concepts, training, documentation, governance, and mentorship. Collaborate with partners, sales, engineering, and vendors to drive successful implementations, security compliance, innovation, and adoption of AI, automation, and omnichannel contact center technologies.

Leads cybersecurity engineering teams developing enterprise security solutions across multiple technology domains. Clarifies security requirements, supports multi-level network security, performs threat modeling and vulnerability assessments, and advises leaders during security risks. Uses and validates enterprise-authorized AI for risk analysis, documentation, security testing, and control validation while ensuring data sensitivity, auditability, and resiliency. Coaches, develops, hires, and recognizes engineering talent.

Build and operate production-grade agentic AI and large language model services for Finance workflows. Responsibilities include developing AWS cloud-native platforms, retrieval and context-engineering capabilities, APIs, front-end integrations, evaluation and observability systems, and scalable event-driven services. The role also involves optimizing performance and cost, implementing secure AI guardrails, troubleshooting distributed systems, and contributing to technical standards, documentation, code reviews, and responsible AI development practices.

Leads architecture for international infrastructure platforms, shaping target-state designs, technical governance, technology evaluations, secure production code, and engineering standards. Provides technical direction across teams, reviews and debugs code, and influences product and operational decisions. Uses enterprise-authorized AI to accelerate architecture analysis, program planning, risk synthesis, governance, and reporting while validating outputs and protecting sensitive data. Requires strong software architecture, development, testing, operational stability, communication, and cross-regional technology experience.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production systems involving NLP, document AI, OCR, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices. Partner with senior stakeholders, define roadmaps and success metrics, improve engineering standards, conduct technical reviews, and mentor engineers.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads Fenergo product consulting engagements focused on AML, sanctions, data analytics, and regulatory risk solutions. Manages client accounts, oversees project planning and execution, guides analytics initiatives, and develops data-driven business strategies. Uses predictive analytics, machine learning, statistical modeling, and scenario analysis to generate insights. Manages and mentors teams, engages stakeholders, resolves conflicts, promotes innovation, and ensures high-quality deliverables and adherence to professional standards.

Leads the organization’s analytics strategy, advanced analytics solution development, predictive modeling, machine learning, data governance, and performance measurement. Partners with business leaders to drive data-informed decisions, growth, efficiency, and innovation. Oversees regulatory data practices, recruits and develops analytics talent, establishes KPIs, and fosters a culture of continuous improvement and data-driven decision-making.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads the design, experimentation, productionization, and optimization of machine learning, deep learning, generative AI, LLM, and agentic systems. Develops Python-based proofs of concept and production solutions, evaluates and tunes models, integrates generative AI into ML platforms, improves system performance, and partners with product and engineering teams. Acts as an ML subject matter expert while mentoring practitioners and influencing product and technical decisions.

Leads technical program delivery for AI, machine learning, and data platforms. Elicits and validates requirements, designs technical solutions, analyzes data, improves processes, identifies automation opportunities, manages risks and dependencies, and reports project status. Partners with product, business, operations, and engineering stakeholders, mentors analysts, supports UI improvements, and provides strategic guidance on complex initiatives.

Own the strategy and roadmap for an ML platform supporting batch inference, real-time serving, and language model hosting. Drive self-service model deployment, monitoring, governance, explainability, reliability, and cost management. Partner with engineering, data science, infrastructure, risk, and compliance teams to accelerate production delivery, define KPIs, and ensure high-quality execution. The role also requires understanding production ML systems, language model operations, observability, responsible AI, and regulated-industry governance.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.

Leads architecture and engineering for consolidating global wide area networks into a unified platform. Defines carrier-scale routing, traffic engineering, resiliency, automation, telemetry, validation, and AI-assisted operational strategies. Oversees transformation plans, migrations, production rollouts, technical standards, risk controls, and reliability improvements while partnering across engineering, infrastructure, cloud, security, operations, and architecture teams.

Designs, builds, and scales enterprise data and AI engineering platforms for a financial institution. Responsibilities include distributed data systems, high-volume pipelines, vector search, RAG, LLM integrations, AI agents, model serving, monitoring, and cloud infrastructure. Partners with data scientists, product managers, architects, cybersecurity, risk, and business teams to deliver secure, scalable, production-grade AI and data solutions.

Leads the design, development, and maintenance of scalable machine learning platforms and infrastructure. Builds tools for model training, deployment, monitoring, and lifecycle management; integrates data engineering and model-serving capabilities; develops secure APIs and automation pipelines; and improves reliability, scalability, and performance. Partners with data scientists, ML engineers, and product teams while driving MLOps, CI/CD, infrastructure automation, and AI-assisted engineering practices.

Leads North American insights, analytics, and AI delivery for underwriting teams. Translates business priorities into data products and analytics solutions, drives measurable value and adoption, oversees AI-enabled transformation, partners with underwriting and enterprise Data & Analytics leadership, and develops high-performing teams. The role also promotes responsible AI governance, agile delivery, scalable solutions, and evidence-based decision-making across underwriting and portfolio management.

Leads analytics and AI initiatives for North American underwriting teams, translating business needs into analytical requirements and practical solutions. Performs hands-on analysis of complex datasets, recommends appropriate analytical methods, prototypes AI-enabled solutions, and partners with data, technology, and business teams through agile delivery. Communicates findings to technical and nontechnical stakeholders, supports model deployment and monitoring, ensures governance and explainability, and drives measurable adoption and business outcomes.

Design, productionize, and operate LLM-powered agents on NEO across CIB and Payments. Build production-grade Graph RAG, agent memory, entitlement-aware context, multi-agent workflows, tool integrations, evaluations, guardrails, and observability. Deploy solutions on AWS or Azure while meeting security, resiliency, compliance, and SDLC standards. Partner with technical, product, and business stakeholders to deliver supported AI agent products.

Leads the end-to-end development and productionization of AI/ML solutions for trade surveillance and regulatory compliance. Responsibilities include model development, LLM and generative AI experimentation, MLOps pipelines, data engineering collaboration, API integration, monitoring, responsible AI governance, and technical mentorship. The role guides projects from requirements through implementation and delivery while partnering with data science and software engineering teams.

Lead cybersecurity strategy and architecture across products, applications, and technologies. Advise engineering teams, communicate complex risks to leadership, develop reusable security frameworks, create mitigation solutions, and evaluate emerging technologies. The role requires influencing cross-functional teams, shaping enterprise-wide architecture, and applying cloud-native, application, API, web, and mobile security expertise across a complex financial-services environment.

Own delivery of enterprise AI automation projects from planning through rollout. Manage scope, timelines, milestones, budgets, risks, dependencies, client communication, status reporting, and change orders. Coordinate cross-functional teams and stakeholders, lead project meetings, track action items through completion, support user adoption and production handoff, and identify process improvements using AI tools.