886 Aws Cloud Security Engineer jobs in New York,NY

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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 AWS Redshift Data Engineer (Contract | 1099)

BrainTrust

New York, NY 10001

New, Posted 1 day ago

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Senior Vice President, Infrastructure Engineer

BNY

New York, NY 10007

New, Posted 1 day ago

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Principal Engineer - Python API Development

Fidelity Investments

Weehawken, NJ 07086

~ 12 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.

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Manager, SAP S4 Public Cloud Asset Management (A2R) Lead

KPMG

New York, NY 10025

Onsite

Minimum six years of recent experience in external management consulting, including at least two full lifecycle SAP S/4HANA Public Cloud implementation with responsibility for Asset to Retire (A2R) and Asset Accounting processes
Experience supporting at least two S4 Public Cloud implementations involving fixed asset accounting, capital projects, CIP, asset capitalization, depreciation, and retirements, with the ability to contribute to fit to standard workshops and business centric solution design
Strong understanding of SAP S/4HANA Public Cloud Asset Accounting configuration, including asset classes, depreciation areas, capitalization rules, investment measures, and integration with Financial Accounting and Controlling
Proven delivery execution skills across SAP Activate phases, including design, build, testing, cutover, and hypercare, with experience supporting asset reconciliation, period end close, and audit readiness
Travel may be up to fifty to eight percent
Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa)

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New, Posted 1 day ago

DiversityJobs

Software Engineering/Applications Leadership Advisory - Executive Technology Services for Global Enterprises

Gartner

Newark, NJ 07175

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Senior Distinguished Engineer, AI Compute (Remote Eligible)

Capital One

New York, NY 10001

Remote

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Head of AI Data Science, Intelligence Ventures

Spectrum

New York, NY 10036

OnsiteUrgently Hiring

Deep expertise in transformer-based sequence modeling and its application to behavioral or interaction data at consumer scale — including architecture design, training methodology, fine-tuning, and embedding quality evaluation
Proven track record developing and deploying household- or user-level embedding models applied to real-world use cases in media, marketing, commerce, and/or customer intelligence — not just research environments. Demonstrated understanding of the unique characteristics of behavioral sequence data: sparsity, temporal dynamics, multi-entity structure, and the signal differences between behavioral intent and explicit interaction
Strong command of the full data science lifecycle in production settings — from exploratory data analysis and feature engineering through model training, validation, deployment, monitoring, and iteration — at large dataset scale (billions, even trillions of records)
Hands-on proficiency with Python, PyTorch or TensorFlow, and distributed ML training frameworks; experience running ML workloads on cloud platforms (AWS SageMaker, Snowflake Cortex, Databricks, or equivalent)
Experience designing and operationalizing feature stores and predictive modeling pipelines that serve downstream intelligence products, audiences, or decision systems in production environments
Ability to communicate complex AI/ML concepts clearly to non-technical executive audiences, product stakeholders, and external partners; comfort operating as an external-facing technical spokesperson for the platform's modeling capabilities and intelligence differentiation
Track record of leading and growing high-performing data science teams; experience recruiting and developing senior ML talent in competitive markets
Genuine intellectual curiosity about the application of AI to behavioral science, consumer intelligence, and agentic systems; awareness of the evolving landscape of foundation models, retrieval-augmented generation, and multi-agent AI architectures
Bachelor's Degree in Computer Science, Statistics, Mathematics, or a related quantitative field
Experience leading applied ML or data science teams building consumer-facing or enterprise intelligence products — 7 years
Hands-on experience designing and training transformer or deep learning models on sequential behavioral data at scale — 5 years
In-office position preferably based in New York City
Travel as required for partner engagements, executive meetings, and industry events

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New, Posted 12 hours ago

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Sr. Data / M/L Engineer [211066]

Skill

New York, NY 10261

Hybrid

Strong SQL and database management experience
Experience with data pipelines and ETL processes
Proficiency in machine learning and statistical modeling
Experience with BI tools (Power BI, Tableau)
Full-stack development knowledge
Experience with cloud platforms (AWS preferred)
Data architecture
Data modeling n
Infrastructure setup
Ability to work with large-scale datasets
Programming experience (likely Python/R inferred)
Strong problem-solving and analytical skills
Bachelor's degree in Electrical Engineering or Computer Engineering
The role requires full stack knowledge, including front-end and back-end components, and the ability to set up new infrastructure.

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Head of Technology, Intelligence Ventures

Spectrum

New York, NY 10036

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

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