Machine Learning Jobs in Jersey City, NJ

Designs and delivers enterprise workflow automation, AI, process mining, and intelligent document processing solutions. Partners with stakeholders to identify automation opportunities, gathers requirements, develops and tests solutions, and manages deployment and post-production support. Uses Pega, Power Platform, UiPath, Alteryx, Python, Signavio, and AI technologies to improve operational efficiency, employee experience, compliance, and measurable business outcomes.

Lead development of wholesale credit risk models supporting CECL, CCAR, client ratings, and regulatory risk management. Build scalable quantitative methods, high-performance computing solutions, analytics frameworks, and modeling pipelines using large datasets and machine learning. Assess model performance and risk, document methodologies, and communicate results and limitations to credit, finance, technology, governance, and regulatory stakeholders.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads the design, experimentation, evaluation, and productionization of machine learning, deep learning, NLP, LLM, and generative AI solutions. Develops production-ready Python code, optimizes model performance, integrates generative AI into ML platforms, and partners with product and engineering teams. Provides technical leadership, mentors practitioners, and drives scalable, measurable solutions using advanced techniques such as fine-tuning, embeddings, RAG, prompt engineering, and distributed training.

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 MLOps engineering for recommendation and personalization systems. Build and maintain distributed GPU training, batch and real-time inference, hyperparameter tuning, model monitoring, observability, validation, LLM quantization, vector database infrastructure, and scalable AWS deployments. Collaborate with data scientists, product, architecture, and engineering teams to deliver high-throughput, low-latency machine learning solutions.

Lead design and adoption of cloud-based, portable AI platform architectures across hybrid environments. Advise on technology selection, document target-state designs, shepherd architecture reviews, develop multi-year roadmaps, integrate enterprise AI capabilities, produce secure production code, and align engineering with architectural governance and controls.

Lead design, develop, and deploy secure, scalable full-stack applications (Java or Python) for finance planning and analysis. Drive architecture reviews, operational stability, CI/CD automation, cloud-native AWS deployments, and integration of AI/ML/LLM capabilities. Mentor teams and lead engineering communities of practice.

Leads engineering for a regulatory and financial reporting platform. Designs scalable architecture and infrastructure, develops secure Python or Java applications, applies AI-assisted development and agent workflows, and supports deployment, monitoring, reliability, and operational stability. Partners with technology, product, and reporting stakeholders on data flows, controls, integrations, and reporting solutions. Requires strong cloud, DevOps, SQL, data modeling, and regulatory reporting expertise.

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.