Top Senior Platform Engineer Jobs in NYC, NY

Operate and evolve a Kubernetes/CNCF platform, own multi-cloud (Azure/AWS/GCP) ops, implement IaC (Pulumi), manage regional data-residency architecture, improve reliability and cost via SLOs, observability, autoscaling, and lead on-call and incident response while partnering with SRE/Foundation teams.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Design, implement, optimize, and maintain scalable hybrid multi-cloud Kubernetes platforms operating at massive scale. Integrate open-source technologies, improve platform reliability, provide technical direction, mentor engineers, and participate in on-call support. The role requires expertise managing large Kubernetes clusters, observability tools, Linux environments, public clouds, custom data centers, and AI-enabled workflow improvements.

Own and evolve AWS cloud infrastructure supporting a financial and hospitality technology platform. Design highly available systems, deployment pipelines, observability, security controls, and infrastructure migrations. Lead incident response, reliability planning, capacity management, and root-cause analysis while participating in on-call rotations. Partner with application engineers, establish infrastructure standards, and mentor platform engineers as the organization scales.

The Senior Platform Engineer will develop and support Workday solutions, collaborate with various teams, and enhance human resource technologies for Capital One.

Lead design, build, and operate high-performance Linux platforms for low-latency electronic trading. Perform deep performance engineering (kernel, CPU/NUMA, networking), write automation and tooling, architect deployment models (containerization, immutable infra), leverage Agentic AI, interface with vendors, mentor teams, and ensure governance, reliability, and reusable solutions across businesses.

Designs and owns Webflow’s foundational cloud and source-control infrastructure, including multi-account landing zones, networking, DNS, PKI, secrets, Infrastructure as Code, GitHub delivery pipelines, self-service hosting patterns, policy guardrails, observability, cost controls, and security baselines. Partners with SRE, security, and developer productivity teams to improve reliability, scalability, adoption, and performance while managing cloud vendors and tooling relationships.

Design, implement, and maintain scalable hybrid multi-cloud Kubernetes platforms at massive scale. Ensure high reliability, integrate open-source observability tools, provide technical direction, operate large Linux environments across cloud and data centers, handle on-call duties, and mentor junior engineers.

Build and operate secure, reliable, scalable platform infrastructure across AWS, PostgreSQL, ECS, CI/CD, and observability systems. Lead security and reliability initiatives, incident response, engineering tooling, and non-functional standards. Develop AI platform capabilities, including LLM development tools, guardrails, and infrastructure for AI-powered product features. Collaborate cross-functionally to understand organizational needs and improve developer productivity.

Lead the design, development, deployment, and support of scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize AI training and inference for latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to technical strategy and mentoring stakeholders.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Drive build-versus-buy decisions, optimize production AI systems for scalability, cost, latency, and throughput, and shape long-term technical roadmaps. Manage, mentor, and grow an engineering team while partnering with scientists, product managers, and technical program managers.

Build and support production AI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or mentor engineering teams.

Leads cross-functional technical projects and owns, scales, and improves platform and business operations systems. Builds production features, evaluates technologies, designs scalable architectures, enhances automation and analytics, collaborates with customers and internal stakeholders, and drives adoption and operational outcomes in a fast-paced SaaS environment.

Administer multi-tenant Kubernetes platforms, manage application deployments, troubleshoot incidents, and ensure system reliability while collaborating with development teams.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.

Own the architecture and technical direction of Metropolis Recognition Platform, spanning edge ingestion, identity resolution, real-time decisioning, notifications, and operator tools. Design scalable streaming pipelines, rules systems, privacy and consent models, observability, testing, and progressive delivery. Lead RFCs, cross-functional alignment, architectural reviews, mentorship, and production quality across application, computer vision, data, security, platform, and partner teams.

Build and evolve core platform components, including data modeling, integration testing, release tooling, internal developer tools, and reliable services. Architect systems, lead initiatives, mentor engineers, partner across teams, and improve scalability, performance, fault tolerance, observability, and production reliability. The role also involves developing AI-enabled developer tools and independently driving complex projects in a fast-paced startup environment.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

Design, build, and maintain platform/DevOps tooling and operational systems. Ensure availability, resilience, security, and compliance. Automate deployments, monitor platform health, support customers, and collaborate with architecture and software teams to align platform configurations and improvements.

Own the taxonomy and attribute platform end to end, including LLM extraction, dbt transformations, ClickHouse data models, Dagster orchestration, full-stack applications, APIs, CI/CD, data quality, observability, and reliable write workflows. Architect AWS infrastructure with Terraform, build AI evaluation and prompt-versioning systems, manage cost and latency, and establish engineering standards, testing, documentation, and operational runbooks.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Lead and maintain platform infrastructure and DevOps tools, manage Google Workspace, troubleshoot user issues, monitor performance and security, automate operations using Python/Bash, container orchestration (Docker, ECS), and CM tools (Ansible, Terraform), collaborate with Architecture and Software teams to ensure platform availability, resilience, scalability, and improved user experience.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

Hands-on technical lead designing, building, and optimizing high-performance Linux compute platforms for low-latency electronic trading. Responsibilities include kernel- and hardware-level performance tuning, containerization, IaC and CI/CD design, automation and tooling development, vendor interfacing, AI-assisted engineering governance, and mentoring cross-functional engineering teams to deliver reliable, low-latency infrastructure.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.