AI Engineer

• Own the enterprise AI Governance architecture roadmap and technical implementation for OneTrust and associated GRC platforms.
• Define and lead enterprise architecture for AI Governance and Cyber GRC workflows.
• Own end-to-end AI governance lifecycle design: intake, classification, risk tiering, review/approval gates, exception handling, monitoring, and periodic reassessment.
• Translate policy and regulatory obligations into technical control patterns and reusable architecture blueprints deployable across UAT and production.
• Design and govern resilient API integrations across OneTrust, Archer, and broader enterprise systems.
• Establish role, group, and organizational segmentation architecture to enforce least-privilege access and scalable governance operations.
• Lead architecture decisions for automation, error handling, observability, incident response, and production support readiness.
• Chair cross-functional architecture reviews and influence risk, compliance, and engineering decision forums on control design and prioritization.
• Mentor engineers and analysts through architecture standards, implementation playbooks, and design quality reviews.

AI GRC Architect

• As a Senior AI Engineer within Bristol Myers Squibb's AI Venture Studio team, you will be a hands-on individual contributor responsible for building secure cloud-hosted applications.
• Design APIs, services, infrastructure patterns, deployment pipelines, and semantic-layer evolution patterns for agent context engineering.
• Collaborate with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service-level behaviors for AI-powered user experiences.
• Create and maintain CI/CD pipelines, environment configuration, automated tests, and release processes for cloud AI applications.
• Partner with AI Engineers, Data Engineers, Data Scientists, and product teams to solve complex delivery challenges.
• Continuously refine delivery priorities and deliver MVP transition activities by maturing AI capabilities.

Senior AI Engineer

• Architect, prioritize, and execute high-volume pipeline of AI initiatives
• Translate complex marketing challenges into secure, scalable AI assets
• Proactively embed within integrated marketing teams and map operational pipelines
• Design, build, and deploy custom Gems, multi-agent orchestrations, and RAG workflows
• Enforce prompt engineering standards, data privacy, and ethical guardrails
• Identify high-potential, localized workflows and scale them into enterprise-wide assets

AI Strategy and Implementation Lead

• You will be building, optimizing, and deploying state-of-the-art agentic AI/ML models and systems within the Verizon Consumer Group (VCG) AI team
• Applying hands-on technical expertise, you will be implementing next-generation customer success, sales, and digital agent solutions that directly impact how millions of customers interact with Verizon
• Working as part of a high-performing squad, you will turn architectural designs into production-grade code while ensuring reliability, performance, and scale
• Developing, testing, and scaling agentic AI/ML solutions, specialized workflows, and LLM-powered systems for VCG platforms
• Integrating AI agent frameworks with existing customer success, digital, and retail channel technologies
• Collaborating with product managers and GTS engineering teams to implement and iterate on production-grade technical roadmaps
• Participating in engineering code reviews, technical design sessions, and red-teaming exercises to ensure safe and reliable model behaviors
• Optimizing model inference pipelines, API endpoints, and data integration layers to meet high-performance consumer demands
• Assisting in technical research and rapid prototyping to quickly evaluate and implement emerging open-source and proprietary AI frameworks
• Contributing to the documentation of technical specifications and creating materials to showcase pilot performance results

• Lead the technical vision, architectural design, and execution of cutting-edge agentic AI/ML solutions within the Verizon Consumer Group (VCG) AI team
• Shape the future of customer experience by building intelligent, autonomous agents that power digital channels, sales, and customer success ecosystems
• Translate long-term strategy into robust, scalable, and secure production systems with cross-functional leadership
• Drive technical strategy, research, and application of advanced agentic AI, large language models (LLMs), and cognitive frameworks
• Design and implement production-grade, highly scalable AI/ML pipelines and agent architectures
• Collaborate with Global Technology Architecture, Security, and Legal teams to ensure data privacy and compliance
• Partner with Customer Service Operations and VCG Strategy teams to identify high-impact business applications
• Develop and maintain a technical roadmap for agentic systems across VCG
• Mentor engineers and establish engineering best practices

Principal AI Engineer

• use technical expertise in data science and real-time AI systems to lead a team focused on the production lifecycle, health, and optimization of deployed models
• ensure commercial AI applications remain highly accurate, safe, and operationally efficient
• monitor and observe real-world model performance to proactively detect model drift, track latency, and manage computational expenses
• design and implement continuous evaluation loops to assess model accuracy on a day-to-day basis
• manage incident response protocols and automated safety guardrails
• establish fallback architectures to ensure a seamless experience if a model fails
• automate continuous deployment (CI/CD) pipelines
• operate as a technical thought leader and trusted advisor to partner with broader data science teams and senior leadership

Associate Director, AI Engineering – Live Operations

• Apply advanced technical expertise in data science, system orchestration, and software engineering to maintain the stability, safety, and efficiency of deployed AI models
• Monitor production model behavior to identify data drift and model degradation, ensuring high predictive accuracy over time
• Implement automated continuous evaluation loops that capture user feedback to dynamically assess performance
• Deploy real-time safety filters, system guardrails, and input/output moderators to mitigate model hallucinations
• Integrate robust fallback systems to preserve end-user experience
• Develop automated continuous integration and deployment pipelines to retrain, validate, and release updated models
• Resolve production model incidents as a senior escalation contact, diagnosing issues with live models
• Collaborate with platform architects and core data science teams to scale backend architectures across cloud, on-premises, and edge network infrastructures

Senior AI Engineer – Live Operations

• Defining an architectural vision and solution aligned to the enterprise architecture strategy, technology, and platform choices.
• Describing the solution intent/associated operating environment, evaluating system impacts, determining the primary systems/sub-systems and their interfaces, and assisting with end-to-end solution design.
• Defining non-functional requirements/architectural runway to support new epics/features and ensuring solutions are fit for purpose by working with stakeholders and service providers.
• Deploying vendor AI platforms in an on‑prem OpenShift environment, integrating AI capabilities across the enterprise ecosystem, and applying Responsible AI and governance standards without owning policy or assessment responsibilities.
• Consulting with the business and operations to identify and understand challenges and opportunities to improve the use and effectiveness of technology,
• Performing design and code reviews to ensure all non-functional requirements for a solution are sufficiently met.
• Leading rapid shaping of a high level architecture with details filled in with emerging business requirements and ensure architecture is flexible, modular, and designed to adapt easily.
• Educating team members on the technology practices, standardization strategies, and best practices to create innovative solutions.
• Clarifying the architecture and assisting with system design for the development teams to support implementation and providing solution options to resolve any architectural impediments.

Architect – AI Platforms Implementation, Integration

• Focus on AI Architect profiles rather than Senior/Lead Data Scientist profiles and with a Pharma/Life Sciences background, with Snowflake Cortex as a good-to-have skill.
• Work Type: Hybrid with min 4 days onsite per week

AI Architect

• Collaborate with cross-functional teams to deliver high-quality AI-driven applications
• Design scalable systems and justify technical decisions
• Implement event-driven architecture and asynchronous workflows
• Mentor team members on AI engineering best practices
• Drive technical leadership initiatives within the team

Senior Full Stack Engineer – AI, Next.js

• Deliver agentic AI solutions in production.
• Design, build, and ship AI agents for a partner business domain using the team’s internal platform and reusable components — owning the solution from first workshop to production operation.
• Partner deeply with the business. Embed with business organizations to understand their workflows, data, and operational touchpoints; identify where agentic AI delivers measurable improvement; and translate domain knowledge into technical designs.
• Build quickly and iterate. Compose solutions from platform capabilities rather than rebuilding from scratch; prototype fast, validate with users, then harden for production. Speed with rigor is the job.
• Apply state-of-the-art techniques. Use foundation models, LLM orchestration, retrieval, and transformer-based approaches for sequential and transactional data — choosing the simplest technique that ships.
• Own production quality. Build the evaluation, observability, and guardrail systems for everything you ship; ensure deployed agents meet the bank’s reliability, security, and compliance standards.
• Strengthen the platform. Contribute improvements, patterns, and reusable components back to the platform team so every delivery makes the next one faster.
• Communicate outcomes. Translate technical progress into presentable results — supporting partner stakeholder reviews and executive demos with clarity and credibility.

Lead Applied AI Engineer

• use innovative data analytics and machine learning techniques to extract valuable insights
• identification, collection, extraction of data from various sources
• performing data cleaning, wrangling, and transformation
• development and maintenance of efficient data pipelines
• design and conduct of statistical and machine learning models
• collaborate with business stakeholders to seek out opportunities to add value from data
• manage and mitigate risks through assessment
• demonstrate comprehensive understanding of the organisation functions

VP – AI Engineering Lead

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• As a Senior AI Engineer on the AI Venture Studio team, you will be a hands-on senior individual contributor who leans into artificial intelligence (AI) to design and build impactful solutions to transform patients’ lives.
• This role is accountable for the AI-first engineering of cloud-native applications, agentic AI products, and the knowledge and context infrastructure that powers them.
• You will have access to the latest AI-centric tools and technologies to support activities such as the design of APIs and MCPs, cloud services orchestration, agent runtime deployment, workflow pipeline implementation, and reusable platform pattern development that enables AI Accelerator projects to move fast without giving up reliability, observability, security, or enterprise architectural alignment.
• This role lives inside the AI Accelerator delivery model: six fully agile two-week sprints across a twelve-week cycle to build, test, validate, and prepare minimum viable products (MVPs) for broader organizational adoption and scaling.
• AI Accelerator projects focus on the most challenging and highest-upside pharma-specific problems across R&D, Commercialization, Manufacturing, and Enabling Functions where critical context is buried in unstructured knowledge files, multimodal documents and reports, operational records, and scientific evidence packages.
• Ship in cycles by demonstrating engineering progress and lessons learned every two weeks.
• Focus on AI-first solutioning that prioritizes BMS technology investments (AWS, Claude, LangSmith, etc.) with the best chance of meeting use case and project success metrics.
• Collaborate effectively with other engineers (AI, data, UI/UX, machine learning) and broader agile AI accelerator product teams.
• Demonstrate a curious and inquisitive mindset with broad technical adaptability while staying hands-on with frontier AI technologies, AI coding agents, and the latest agentic engineering capabilities.

• Develop, test, maintain, and enhance software applications and components using modern programming languages such as Python, JavaScript, Java, Go, or similar languages.
• Write clean, secure, maintainable, and efficient code following established software development standards and best practices.
• Collaborate with engineers, product owners, and business users to understand requirements and translate them into technical solutions.
• Troubleshoot, debug, and resolve application issues, escalating risks or complex problems when needed.
• Participate in code reviews and contribute to improvements in code quality, testing, and development practices.
• Support integration of applications with APIs, databases, cloud services, and software tools.
• Use AI-assisted development tools or AI agents to support coding, testing, debugging, and documentation.
• Contribute to continuous improvement of development processes, agile engineering practices, and secure application development.

Software Engineer, AI Engineering – Enablement

Machine Learning Engineer

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Own and manage the enterprise AI use case intake, triage, and governance workflow across Verisk.
• Serve as the primary governance contact for teams submitting AI use cases to the AI Governance Board for approval.
• Guide requesting teams through required documentation, registry metadata, risk assessment, stakeholder review, control requirements, approval steps, and ongoing governance obligations.
• Evaluate AI use cases for completeness, business purpose, data sensitivity, customer or regulatory impact, model/system risk, third-party involvement, and readiness for governance review.
• Track AI use cases from intake through triage, review, approval, implementation, monitoring, remediation, and closure.
• Serve as Verisk's business owner and day-to-day operational lead for the AI Governance Platform.
• Lead the internal side of the AI Governance Advisory engagement, including readiness activities, workshop participation, documentation requests, stakeholder coordination, deliverable review, and follow-up actions.
• Maintain AI use case inventory, workflows, intake forms, questionnaires, assessments, risk classifications, stakeholder reviews, approval records, evidence, controls, remediation items, and reporting in the AI Governance Platform.
• Partner with the AI Governance tool vendor and internal teams to configure and validate workflow stages, questionnaires, automations, conditional routing, risk types, risk scenarios, policy packs, controls, reports, and self-service intake capabilities.
• Administer and coordinate AI Governance Board operations, including agenda planning, meeting scheduling, presentation preparation, decision logs, minutes, record keeping, and follow-up actions.
• Translate the stakeholder operating model into clear roles, responsibilities, review expectations, escalation paths, and handoffs at both program and use-case levels.
• Prepare AI use cases for board review by developing concise summaries, risk profiles, issue logs, stakeholder input, open questions, and recommended decision points.
• Track board decisions, conditions of approval, exceptions, remediation items, escalations, unresolved risks, and required evidence.
• Coordinate with Legal, Privacy, Compliance, Information Security, Data Governance, Procurement, Vendor Management, Third-Party Risk, Enterprise Risk, Technology, Audit, and business stakeholders to ensure reviews are timely and complete.
• Support the AI Governance Board and cross-functional teams to implement Verisk’s AI governance policies, standards, procedures, templates, and guidance.
• Support Verisk's global AI governance strategy and help ensure AI systems are developed, deployed, and monitored in a responsible and transparent manner consistent with Verisk's Ethical AI Policy.
• Maintain AI governance framework components, including intake standards, risk assessment criteria, questionnaire logic, inventory requirements, stakeholder routing, approval standards, reporting processes, and control documentation.
• Partner with Legal, Compliance, Privacy, Information Security, Procurement, and Third-Party Risk on policy mapping, vendor AI addenda inputs, policy-pack interpretation, and control expectations, while ensuring formal legal and policy decisions remain with the appropriate owners.
• Develop recurring and ad hoc AI governance reporting for the CDO office, AI Governance Board, senior leadership, risk committees, business stakeholders, and other internal audiences.
• Translate AI Governance reporting outputs into stakeholder-ready views that show governance status, reviewer actions, pipeline trends, risk tiers, open issues, and executive decision points.
• Track and report key metrics such as AI use case volume, review status, approval cycle time, risk tiering, business unit participation, stakeholder review status, remediation items, policy exceptions, audit findings, and board decisions.

Director, AI Governance Lead

• Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure.
• Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked.
• Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
• Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load.
• Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff.
• Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction.
• Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project.
• Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it.
• Own MLOps incident response — production model failures are SEV events with postmortems.
• Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.
• Integrate LLM APIs (Bedrock, Anthropic, OpenAI) into production paths — RAG pipelines, agent eval frameworks, prompt versioning, cost and latency observability.

Senior Machine Learning Operations Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

AI Research Scientist

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration.
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks.
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation.
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities.
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence, such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses.
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence.
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning.
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions.
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives.
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS).
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery.
• Co-author manuscripts, concept papers, and translational strategy documents.

Machine Learning Engineer jobs in New Jersey

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AI GRC Architect

Senior AI Engineer

AI Strategy and Implementation Lead

Senior AI Engineer

Principal AI Engineer

Associate Director, AI Engineering – Live Operations

Senior AI Engineer – Live Operations

Architect – AI Platforms Implementation, Integration

AI Architect

Senior Full Stack Engineer – AI, Next.js

Lead Applied AI Engineer

VP – AI Engineering Lead

Senior Software Engineer – Applied AI

Senior AI Engineer

Software Engineer, AI Engineering – Enablement

Machine Learning Engineer

Director, AI Governance Lead

Senior Machine Learning Operations Engineer

AI Engineer

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration