667 Cloud Detection Engineer jobs in New York,NY

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Manager, SAP S4 Public Cloud Professional Services Automation (PSA)

KPMG

New York, NY 10001

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Sr. Backend Software Engineer (Video Engineering)

Philo

New York, NY 10025

Flexible Schedule

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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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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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