Machine Learning Jobs in Jersey City, NJ

Lead cybersecurity architecture for enterprise applications and platforms: evaluate principles and controls, design cloud-native secure solutions, provide technical guidance, influence stakeholders, apply threat modeling and frameworks, and build reusable security patterns and guardrails to improve security outcomes.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

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 Data Architect responsible for defining and evolving data architecture and standards across 100+ applications, driving adoption of strategic data lake platforms, designing target-state architectures and data exchange patterns, mentoring junior architects, and ensuring governance, data quality, and cloud-based database best practices.

Lead a software engineering team to design, build, and maintain secure, resilient Java-based applications. Develop and review production-quality code, drive adoption of AI-assisted engineering practices, automate remediation and SDLC tooling, evaluate vendor solutions, and coach engineers on secure and responsible AI use. Promote reuse, operational stability, and agile/CI-CD practices within financial services applications and cloud-native environments.

Design, develop, test, and maintain secure, high-quality production software. Create architecture and design artifacts, analyze large datasets to improve systems, troubleshoot complex problems, and contribute to engineering communities and inclusive team culture.

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Designs and delivers production agentic AI platforms and large language model services for Finance workflows. Responsibilities include building agents, tool integrations, retrieval and context-engineering systems, secure APIs, and cloud-native AWS services using containers, serverless computing, and event-driven architecture. The role also optimizes performance and cost, establishes evaluation and observability practices, troubleshoots distributed systems, and collaborates on technical standards, documentation, and code quality.

Leads software engineering within JPMorganChase’s Payments organization, designing, developing, testing, and troubleshooting secure, scalable production systems. The role creates architecture and design artifacts, maintains algorithms, analyzes large datasets, improves system architecture and coding practices, and uses enterprise-approved AI-assisted development tools responsibly. Required experience includes Java, Python, SQL, PostgreSQL, AWS, SDLC, agile methods, CI/CD, resiliency, and security. Databricks and machine learning frameworks are preferred.

Build and enhance AI/ML platform capabilities spanning model registries, feature registries, governance, data preparation, annotation, lineage, and auditability. Develop cloud-native microservices, APIs, and large language model-powered workflows using retrieval and agent patterns. Improve platform reliability, security, observability, performance, and developer experience while collaborating with engineers, architects, product managers, data scientists, and research partners.

Lead architecture design and governance for trade surveillance products, provide technical direction, integrate enterprise AI securely and auditable into architectures, develop and review production code, and establish reusable SDLC/toolchain patterns.

Lead design and deliver reusable, cloud-native components and SDKs; write and review secure production code; automate remediation and CI/CD; drive adoption through communities of practice; evaluate vendor and internal architectures, and improve operational stability across the enterprise.

Design, build, and deploy scalable AI and ML solutions by wrangling and integrating data, implementing models (Python/Java, TensorFlow, Scikit-Learn), developing data pipelines and NLP applications, collaborating with clients, and mentoring junior team members to drive business insights and regulatory risk solutions.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

Provide commercial legal support for enterprise cloud, SaaS, and infrastructure transactions. Draft, negotiate, and advise on contracts (SaaS, IaaS, software licensing, DPA), counsel sales and cross-functional teams on legal/privacy/commercial risk, manage concurrent deals, improve templates and processes, and coordinate outside counsel to enable scalable, business-focused legal support for growth.

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.

Lead MarTech strategy and hands-on execution for AI-driven, real-time 1:1 personalization. Evolve and evaluate MarTech stack, integrate AI/ML decisioning, build real-time journeys in Adobe Experience Platform, define XDM schemas and identity resolution, monitor dataflow health, lead Agile delivery pods, translate strategy to technical requirements, and create executive-level measurement and governance frameworks.

Lead and scale MEDHOST’s software engineering organization, overseeing product delivery, modernization, security, reliability, scalability, and operational performance. Develop technology strategy and architecture roadmaps while driving AI-first transformation across products, engineering workflows, and enterprise operations. Establish performance metrics, govern technology investments, manage strategic initiatives, and advise executives on emerging technologies, risks, and opportunities. The role also requires implementing generative AI, machine learning, and AI governance within healthcare software environments.

Own the strategy, roadmap, and delivery of AI-powered Capital Management and Lending tools. Lead engineering teams, prototype AI solutions, translate banking requirements into technical designs, and collaborate with senior stakeholders. Applications include capital allocation, RWA optimization, credit underwriting, predictive modeling, document intelligence, covenant monitoring, and stress testing. Ensure solutions meet security, ethical, regulatory, Fair Lending, Basel III/IV, CCAR, and model risk requirements.

Translates business requirements into scalable AI and GenAI solutions, supporting implementation, model deployment, testing, systems integration, customization, scripting, and troubleshooting. Partners with business users, data scientists, and ML engineers; gathers product feedback; trains teams on prompt engineering and AI operations; and assesses risks involving privacy, bias, ethics, and model limitations. Requires autonomous stakeholder advising and mentoring of junior analysts.

Lead design and delivery of secure, scalable AI/ML and generative/agentic solutions: develop LLMs and agent frameworks, productionize end-to-end models, mentor teams, collaborate with stakeholders, and drive R&D and PoCs to production.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead supply chain analytics consulting engagements, offshore teams, and client discussions. Translate supply chain business challenges into scalable data and analytics solutions using Python, SQL, machine learning, AI, optimization, and operations research. Provide domain expertise across demand and supply planning, shipping, and inventory management; mentor analysts and data professionals; architect analytics frameworks; and communicate actionable insights to business stakeholders.

Develop complaint intelligence, dashboards, reporting, and analytical products for compliance and risk stakeholders. Apply data science, machine learning, NLP, LLMs, and advanced analytics to identify complaint trends, emerging risks, root causes, and customer impact. Oversee complaint data quality, governance, taxonomy, lineage, and controls. Translate complex findings into executive-ready visualizations and narratives while ensuring responsible AI and human-in-the-loop practices.