858 Associate Cloud Engineering jobs in New York,NY

Director, Google Cloud Security, Solution Architect

KPMG

New York, NY 10025

Minimum eight years of recent experience in security architecture
At least four years of hands-on experience designing and implementing security solutions on Google Cloud Platform
Bachelor's degree from an accredited college/university or a minimum of ten years of equivalent experience
Google Cloud Professional Cloud Security Engineer, Professional Cloud Architect, or other relevant Google Cloud certifications are highly preferred
Certifications like CISSP are a plus
Deep technical expertise in Google Cloud security services, including core Google Cloud Platform services, Google SecOps SIEM/SOAR, Security Command Center, and other native security controls
Hands-on experience with Google AI technologies such as Vertex AI and Gemini Enterprise, and their integration with enterprise security solutions
Exceptional client-facing and presentation skills with a demonstrated ability to lead technical sales discussions, build client relationships, and align solutions with business needs
Experience in collaborating with cross-functional teams such as system administrators, data scientists, architects, and cybersecurity engineers to customize solutions
Ability and willingness to travel as needed for client support
Must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future

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Senior Manager, AI Engineering (People Leader) (Gen AI Platform Services)

Capital One

New York, NY 10001

New, Posted 2 hours ago

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Senior Associate, Privileged Access Management Delivery Engineer

KPMG

New York, NY 10001

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Principal Engineer - Python API Development

Fidelity Investments

Jersey City, NJ 07311

~ 22 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.

New, Posted 21 hours ago

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Senior Test Engineering Technician - AI Data Center Systems (Servers & GPUs)

Advanced Micro Devices

Secaucus, NJ 07094

~ 18 min Onsite

Associate degree in computer science, computer engineering, Information technology or equivalent experience preferred.
CompTIA A+ or equivalent experience.
CompTIA Server+ or equivalent experience.
Windows OS experience.

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Head of AlphaGen - Investor Research Product Engineering, Director

Blackrock

New York, NY 10025

OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent practical experience
An advanced degree is a plus
Strategic Engineering Leadership: Proven experience leading large-scale engineering organizations through product transformation initiatives (e.g., platform modernization, cloud migration, operating model changes). Demonstrated success in setting vision and executing across complex, multi-year technology programs
Alpha generation workflows or closely related quantitative research processes in finance/investments
Cloud technologies and cloud-native architectural patterns (e.g., microservices, containerization, distributed computing e.g. Ray)
API-first platform design and developer experience best practices
AI/ML applications for workflow automation, data validation, and operational excellence
Modern stack knowledge and skills - e.g. Python, Polars, Ray, MLFlow or equivalent technology
Demonstrated strength in partnership-building and cross-functional collaboration
Able to influence and align product teams, platform/infrastructure teams, and governance bodies around a common vision and roadmap
Ability to operate in complex market and organizational contexts, translating strategic objectives into actionable plans
Proven track record of driving innovation, navigating ambiguity, and delivering measurable outcomes at scale
AI-First, Human-in-the-Loop: Embraces AI-driven solutions as a default for efficiency and scale, while keeping expert human judgment in critical loops to ensure quality and trust
Platform Over People: Prioritizes building robust platforms and automation over relying on individual heroic efforts, enabling sustainable and scalable growth
Single Front Door: Advocates for a unified user experience where researchers and stakeholders have one intuitive portal for all their needs, simplifying access and support
Standardization Everywhere: Drives consistency in tools, processes, and data across the board, reducing complexity and improving interoperability and maintainability
Self-Service as the Default: Champions self-service capabilities, empowering users to accomplish tasks independently (from data ingestion to model deployment) without bespoke engineering work
Deep Co-Ownership with PMG: Fosters a culture of partnership with the Portfolio Management Group, aligning closely on priorities, sharing roadmaps and KPIs, and co-owning outcomes to ensure the platform truly meets investment teams' needs
For New York, NY Only the salary range for this position is USD$225,000.00 - USD$285,000.00
Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits
BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance
4 days in the office per week, with the flexibility to work from home 1 day a week

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Chief Systems Engineer

L3 Harris

Clifton, NJ 07011

~ 30 min OnsiteBilingual PreferredEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's Degree with a minimum of 12 years of prior related experience.
Graduate Degree with a minimum of 10 years of prior related experience.
In lieu of a degree, minimum of 16 years of prior related experience.
2 years of experience in Cameo and SysML v1
3 years experience with Agile development and using JIRA
Active Secret Security Clearance- Ability to obtain and maintain any additional clearances as required.

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IT Infrastructure Architect, Staff

Curtiss-Wright

Parsippany, NJ 07054

RemotePaid Time OffRetirement Benefit

5-12 years of experience in infrastructure, systems engineering, or related roles
Broad experience in enterprise IT environments (not limited to a single system or tool)
Exposure to core infrastructure areas such as: Identity and access management (e.g., Active Directory or similar)
Server environments (Windows and/or Linux)
Application or platform support
Experience participating in system upgrades, migrations, or integrations
Demonstrated ability to learn and adapt to new technologies quickly
Strong communication and interpersonal skills
Proven ability to produce clear documentation with attention to detail
Exposure to Microsoft environments (Active Directory, Windows Server, SQL Server, Exchange) preferred
Experience with virtualization (e.g., VMware) preferred
Familiarity with cloud platforms (e.g., Azure) preferred
Exposure to CMDB or service management tools preferred
Experience working alongside ERP or business systems teams preferred
Any experience with platform ownership or application support preferred

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Principal Engineer - Python API Development

Fidelity Investments

Harrison, NJ 07029

~ 23 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.

New, Posted 1 day ago

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Photonic Assembly and Test Technician

CACI International Inc

Florham Park, NJ 07932

~ 50 min Onsite

Associates degree in engineering or a closely related technical field, or equivalent combination of education, skill, and experience
Minimum of 3 years of "hands-on" photonic or electrical fabrication, test, and measurement experience
Ability to follow detailed work instructions or test procedures
Prior experience with custom fine assembly
Must be computer savvy and a competent user of Microsoft Office products
Good interpersonal and communication (written and oral) skills
Must be a U.S. person able to receive export-controlled information

SmartExplore AI is experimental.