Top Senior Platform Engineer Jobs in NYC, NY

Build and maintain scalable platform tools, infrastructure, and automation that enable reliable software delivery across engineering teams. Develop C#/.NET solutions, manage Kubernetes environments, implement Helm and GitOps deployment workflows, and support distributed microservices. Troubleshoot complex systems, improve observability and performance, conduct code reviews, collaborate with stakeholders, and mentor engineers while advancing deployment reliability and operational excellence.

Design, operate, and improve highly scalable hybrid multi-cloud Kubernetes platforms supporting business-critical systems. Responsibilities include optimizing reliability, managing large-scale infrastructure, integrating open-source technologies, guiding Kubernetes platform evolution, and mentoring engineers. The role requires expertise with Kubernetes clusters containing thousands of nodes, observability tools, Golang, Linux, AWS, GCP, OCI, custom data centers, and on-call operations. Candidates must be eligible for CJIS clearance and periodically complete government-required background and fingerprint checks.

Own platform infrastructure scaling, reliability, portability, observability, and incident response as Pallet grows to millions of AI agent runs daily. Improve cloud and on-premise deployments, enterprise integrations, developer productivity, CI, local development, and production debugging. Establish SLOs, plan capacity, identify bottlenecks, and lead post-mortems. The role requires broad infrastructure ownership and technical direction in a high-growth startup environment.

Build and operate Cloudflare’s large-scale Kubernetes platform running on bare-metal Linux. Design resilient systems, support application teams, improve resource efficiency, harden infrastructure, and respond to platform incidents. The role combines software engineering with DevOps responsibilities, including production Kubernetes operations, Linux systems debugging, container networking, and contributions to open-source projects.

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Own and improve the company’s platform, infrastructure, CI/CD pipelines, developer experience, automated testing, release processes, fleet observability, access model, and runtime dependencies. The role focuses on reducing release lead time across a large multi-tenant SaaS environment, modernizing infrastructure safely, documenting platform knowledge, and enabling engineers and AI agents to work more effectively.

Build and improve the Enterprise AI operating model through AI-enabled processes, operational reporting, knowledge management, stakeholder forums, intake governance, and asset registry oversight. The role translates complex program information into executive narratives, coordinates decision-making, enables cross-functional alignment, and continuously improves planning, prioritization, governance, communication, and execution mechanisms.

Lead architecture and implementation of reliability improvements across CrowdStrike's cloud-native platform. Build shared libraries and services, drive observability and SLO practices, perform performance and cost optimization, run resilience engineering and chaos experiments, automate infrastructure-as-code, mentor engineers, and embed with product teams to deliver scalable, highly reliable distributed systems at organizational scale.

Build and maintain backend platform systems that connect digital banking software with core banking systems. Develop reliable, performant APIs and support account and transaction data integrity, real-time transaction alerts, transfers, and payment settlement workflows. The role requires collaboration on complex financial integrations and may involve Python, Django, infrastructure automation, and Linux administration.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Lead and grow ReflexAI’s platform engineering: design and automate multi-cloud (GCP/AWS) infrastructure, build developer tooling and CI/CD golden paths, improve observability and on-call ergonomics with Datadog, and strengthen cloud security, secrets management, and compliance to support production-grade AI workloads.

Build and operate foundational platform services, libraries, and tooling for large-scale distributed systems. Lead reliability, scalability, observability, performance, resilience, cost optimization, and infrastructure-as-code initiatives across product teams. Define SLOs and error budgets, resolve complex incidents, improve architecture, mentor engineers, and influence organization-wide technical decisions. The role involves hands-on backend engineering, production coding, multi-cloud platforms, and automation to eliminate operational toil.

Build a greenfield, low-latency algorithmic execution platform in C++. Responsibilities include developing FIX gateways, exchange market data feed handlers, venue connectivity, pre-trade risk controls, platform infrastructure, audit logging, and algorithmic strategy migration. The role requires optimizing critical paths for microsecond-level performance while integrating U.S. exchanges and dark venues. Future work includes multi-asset execution, research and backtesting, and international market expansion.

Architect and operate Affirm’s large-scale Amazon EKS compute platform, including cluster lifecycle, node management, autoscaling, shared components, infrastructure automation, workload safety policies, observability, cost and capacity optimization, and self-service workflows. The role drives technical strategy, mentors engineers, partners cross-functionally, and participates in on-call incident response.

Designs and operates governance, security, deployment, monitoring, and cost-management frameworks for enterprise AI platforms. The role builds semantic models in Snowflake and Databricks, develops CI/CD and infrastructure-as-code solutions, enables multi-environment deployments, monitors AI reliability and usage, and implements auditability, responsible AI standards, RBAC, identity controls, and policy enforcement. It also partners with data and engineering teams to manage lineage, governed metrics, model operations, and cloud consumption across AI workloads.

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.

Manage and scale AWS infrastructure and Ruby on Rails applications; improve reliability, performance, observability, and automation. Responsibilities include Infrastructure as Code, Kubernetes operations, monitoring, code review, production debugging, capacity planning, incident response, and partnering with feature engineering teams to deploy and operate services safely.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Designs and maintains scalable cloud platform services, Kubernetes environments, CI/CD pipelines, infrastructure-as-code, observability, and developer tooling. The role includes incident response, root cause analysis, reliability improvements, cross-team collaboration, technical planning, and mentoring engineers. Candidates should have strong backend and distributed-systems experience, AWS expertise, Terraform or CloudFormation experience, production Kubernetes experience, and familiarity with automation, observability, and operational support.

Leads architecture, automation, reliability, and evolution of secure cloud platforms supporting federal systems. Owns Infrastructure-as-Code, CI/CD, observability, incident response, disaster recovery, and platform standards. Partners across engineering, security, architecture, and product teams; evaluates technologies, leads technical decisions, and mentors engineers. Requires deep DevOps, infrastructure, Kubernetes, Terraform, cloud, scripting, and production troubleshooting expertise, along with technical leadership and ability to obtain Public Trust clearance.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Lead the design and deployment of AI platforms, agentic systems, model gateways, RAG pipelines, and AI-focused DevOps infrastructure. Build self-service GCP AI APIs, SDKs, and Terraform modules; manage model routing, observability, security, deployment, and automation using GKE, Cloud SQL, Cloud Build, and related services. The role requires extensive platform engineering, cloud infrastructure, IaC, MLOps, and enterprise AI architecture experience.

Investigate and remediate platform reliability issues across TypeScript applications, microservices, integrations, Kubernetes workloads, and CI/CD pipelines. Build observability through monitoring, alerting, dashboards, and distributed tracing; diagnose performance issues; conduct resilience and fault-injection testing; define SLOs and SLIs; maintain reliability roadmaps and runbooks; and partner with infrastructure and application engineering teams on complex incidents.