639 Aiml Engineer Cyber Security jobs in North Bergen,NJ

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Head of AI Data Science, Intelligence Ventures

Spectrum - New York, NY

Head of AI Data Science, Intelligence Ventures

~ 20 min Onsite

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Senior Distinguished Engineer, AI Compute (Remote Eligible)

Capital One

New York, NY 10001

Remote

New, Posted 20 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

North Bergen, NJ 07047

OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

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Manager, Cyber Defense

KPMG

New York, NY 10025

~ 20 min Onsite

Minimum five years of recent experience in Cyber Security
Bachelor's degree from an accredited college/university in a relevant discipline
Proficiency in Security Monitoring, Incident Response methodologies, and configuration of various security technologies such as LM, SIEM, firewalls, intrusion prevention systems, AV, and DLP
Hands-on experience with Security Information and Event Management, Log Management, SOC, and Incident Response
Availability for up to eight percent travel
Must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future

SmartExplore AI is experimental.

New, Posted 1 day ago

ResumeLibrary

Head of Engineering for Wealth Management

MassMutual

New York, NY 10012

~ 20 min Onsite

15+ years of progressive technology leadership experience, including leadership of large-scale software engineering organizations.
Experience leading organizations of 75+ engineers across multiple disciplines and geographic locations.
Proven track record modernizing enterprise platforms through cloud-native architectures, microservices, APIs, and event-driven systems.
Deep experience delivering customer-facing platforms in wealth management, capital markets, banking, fintech, retirement, or financial services.
Demonstrated success partnering with executive stakeholders to align technology investments with business outcomes.
Experience managing large-scale digital transformation initiatives with measurable business impact.
Must have Series 99 or ability to obtain it within 6 months.

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Project Engineer

Oerlikon Surface Solutions

Westbury, NY 11590

$75,000-$100,000/yr

~ 49 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's degree in materials science or mechanical engineering
3 years job experience in thermal spray or PVD coating technologies
Must be proficient in using business and communications software (preferably Word, Excel, PowerPoint, common Windows operating systems, and Outlook)
Ability to travel 30% travel may be required
Work Location: In person

SmartExplore AI is experimental.

New, Posted 20 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Weehawken, NJ 07086

~ 7 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

Recommended

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Machine Learning Engineer

Capital One

New York, NY 10001

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Head of Technology, Intelligence Ventures

Spectrum

New York, NY 10036

~ 20 min Onsite

Deep expertise across the full intelligence platform stack — including distributed data pipelines, ML platform architecture, embedding systems, feature stores, agent-to-agent API design, and LLM-powered application layers — with demonstrated ability to architect and ship all layers as a coherent, production-grade product
Demonstrated experience architecting and operating consumer data intelligence or data product platforms underpinned by complex machine learning systems and built on modern, cloud-native data infrastructure
Hands-on proficiency with Snowflake (including Cortex, Native Apps, and data sharing frameworks), cloud data platforms (AWS, Azure, or GCP), and production ML/AI systems at scale
Experience building agentic AI systems and LLM-powered product interfaces — including agent-to-agent APIs, retrieval-augmented generation architectures, and natural language UIs grounded in proprietary data — with strong product instincts around accuracy, trust, and user experience for non-technical enterprise audiences
Proven ability to translate complex technical architecture into clear executive and partner-facing communications; comfortable engaging at the C-suite level and in strategic partner negotiations with hyperscalers and technology platforms
Strong understanding of privacy-preserving data architecture, including differential privacy, de-identification techniques, zero-copy and clean room frameworks, and the regulatory landscape governing consumer behavioral data
Track record of recruiting and developing exceptional engineering talent in competitive markets; experience building high-performance teams from early-stage through scaled operations
Experience managing external development partners and outsourced engineering resources alongside an internal team in a fast-moving, build-from-scratch environment
Ability to operate in an ambiguous, early-stage environment while maintaining rigorous engineering standards and delivering against aggressive milestones
Experience building externally-consumed data or intelligence platforms — including developer-facing APIs, data marketplace integrations, or partner-accessible data products — with strong instincts around developer experience, access governance, and commercial platform design
Bachelor's Degree in Computer Science, Software Engineering, Electrical Engineering, or a related technical field
Experience leading consumer data intelligence, AI/ML platform, or data product engineering organizations — 10 years
Engineering leadership and team-building experience, including managing senior technical staff — 7 years

SmartExplore AI is experimental.

New, Posted 23 hours ago

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