Machine Learning Jobs in Jersey City, NJ

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

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.

Leads large-scale AI and machine learning initiatives, defining problem statements, roadmaps, and production solutions. Develops NLP, generative AI, recommendation, personalization, search, and document question-answering models. Builds scalable batch and real-time ML pipelines on AWS, collaborates with product, engineering, business, legal, and compliance teams, and communicates technical results to senior stakeholders. Oversees experimentation, model evaluation, deployment, and team performance.

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.

Lead design, build, and scale of generative and agentic AI solutions; deliver production ML systems, reusable platforms, observability and MLOps, mentor AI teams, partner with stakeholders to prioritize and measure enterprise impact.

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.

Leads development of secure, scalable Java and AWS software systems, including microservices, event-driven platforms, and production machine-learning systems. Designs data and feature pipelines, model deployment and monitoring, automation, CI/CD, and operational stability practices. Guides code reviews, testing, secure coding, vendor evaluations, and responsible adoption of AI-assisted engineering tools. Collaborates across product, data, platform, SRE, and governance teams while promoting MLOps, LLM, observability, and model-monitoring practices.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Lead design, development, and production deployment of advanced AI/GenAI and LLM solutions. Build and tune models, integrate generative AI into ML platforms, drive MLOps adoption, ensure responsible AI governance, collaborate cross-functionally, and mentor AI engineers.

Lead design, development, and production deployment of AI, GenAI, and LLM solutions. Build and tune models in Python, collaborate across product and engineering, drive MLOps adoption, ensure model governance and regulatory compliance, and communicate results to technical and business stakeholders.

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.

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, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Lead client consulting workstreams focused on demand, supply, inventory, production, capacity planning, forecasting, and S&OP. Assess planning processes, design improvements, support APS and ERP implementations, develop analytics and AI-enabled planning solutions, and manage client relationships, deliverables, timelines, and junior team members. The role requires hands-on supply chain planning experience and collaboration with cross-functional stakeholders.

Lead enterprise data security architecture to protect sensitive data across its lifecycle. Define strategies for encryption, key management, tokenization, masking, DLP, classification, database security, and Zero Trust. Advise execs and engineering on secure design patterns for data platforms (e.g., Starburst), cloud/on-prem systems, APIs, data pipelines, and AI/ML workloads. Develop roadmaps, evaluate technologies, mentor staff, and drive secure-by-design adoption across the organization.

Develop and test software solutions for Equities Technology while supporting organization-wide AI initiatives. The role coordinates business, data, and technology stakeholders, prioritizes AI opportunities, tracks delivery, builds proof-of-concepts, and explains AI concepts practically. Responsibilities include architecture, requirements refinement, CI/CD mentoring, release automation, automated testing, and technical solution delivery. Candidates need hands-on technical experience, AI or data familiarity, strong organization, collaboration, communication, and problem-solving skills.

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.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Design conversational dialogs, prompts, scripts, and interaction flows for virtual assistants, chatbots, and omnichannel digital self-service experiences. Collaborate with product, engineering, research, and business teams in Agile environments; apply user-centered design, research, analytics, and feedback to improve experiences. Work with conversational AI platforms, APIs, datasets, LLMs, NLP, and generative AI technologies while supporting testing, implementation, and measurement under senior designer guidance.

Analyze multimodal healthcare, genomic, clinical, and biomedical imaging data to support drug discovery, biomarker development, disease progression modeling, clinical trial design, and real-world evidence analyses. The role involves building statistical and machine learning models, developing data pipeline modules and interactive tools, collaborating with clinical, genomics, and biology teams, contributing to data strategy, and communicating findings through publications and stakeholder presentations.

Leads and mentors multiple software engineering teams delivering secure, scalable financial technology. Establishes enterprise AI-assisted engineering and SDLC automation practices, improves delivery speed and quality, oversees technical execution, manages resources and budgets, ensures application resiliency and security, and collaborates with stakeholders. Requires hands-on software development, system design, testing, operational stability, and leadership experience using Java, Python, SQL, PostgreSQL, and AWS.

Build and deploy AI and machine-learning capabilities for cyber threat hunting, anomaly detection, incident response, detection engineering, and security automation. Develop custom models, LLM applications, and agentic systems, managing the full production lifecycle from data preparation through monitoring. Partner with security, engineering, offensive security, and governance teams to evaluate emerging technologies, support responsible AI, advise leadership, and mentor technical staff.

Own customer experience product strategy and roadmaps across web and mobile commerce. Lead experimentation, data analysis, usability testing, customer research, and post-purchase experience improvements. Partner with Engineering, Design, Marketing, Merchandising, AI, and Analytics teams to deliver features that improve engagement, conversion, trust, and customer lifetime value. Communicate product vision and priorities to executives while balancing short-term delivery with long-term strategy.

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 the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production applications involving NLP, document AI, OCR, computer vision, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices while mentoring engineers, setting technical standards, and aligning multi-quarter roadmaps with senior stakeholders.