Top Remote Platform Engineer Jobs

Lead and execute the AI infrastructure roadmap for on-prem/private cloud, architecting compute, storage, networking, and accelerators to support large-scale training and low-latency inference. Serve as product owner for AI infrastructure, run RFIs/RFPs, define platform standards (cooling, power dense racks, high-speed networking), partner with data science/MLOps/security/governance, influence executive stakeholders, and evaluate emerging AI infrastructure technologies and vendors.

Lead design, procurement, and scaling of Mastercard's on-premise AI infrastructure. Translate data science needs into platform capabilities, run RFI/RFPs, architect compute/storage/networking for large-scale training and low-latency inference, define infrastructure standards, partner with MLOps, security, and governance, and influence executive stakeholders on AI infrastructure strategy and vendor selection.

Lead design and scaling of Mastercard's on-premise AI infrastructure including compute, storage, networking, and accelerators. Drive roadmap, RFI/RFPs, vendor selection, and cross-functional alignment to support large-scale training and low-latency inference. Ensure compliance with security, governance, and enterprise requirements while influencing senior stakeholders and staying current on AI infrastructure trends.

Lead design, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Define standards for compute, storage, networking, cooling and data center design; run RFI/RFPs; partner with data science, MLOps, platform, security, and governance teams to support large‑scale training and low‑latency inference for generative and predictive AI workloads.

Design, automate, and operate enterprise OpenShift and Linux platforms on high-performance infrastructure. Build and optimize OpenShift/Kubernetes clusters, manage RHEL and KVM hosts, implement storage (GPFS), security, monitoring, and automation (Ansible, scripting). Troubleshoot performance, partner with architects and security teams, and support scalable, highly available solutions including mixed container and VM workloads.

Design, deploy, and operate scalable data pipelines and cloud infrastructure for ML/GenAI, manage day-2 LLM serving platforms, automate IaC and platform maintenance, ensure observability and reliability, and support production ETL and incident response.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

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.

Build and improve Radar's data infrastructure with a focus on customer feedback and successful implementation. Engage with customers to enhance their experience and deploy new solutions.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Build and operate Radar's infrastructure and developer platform to support 1B API calls/day. Work on backend, SDKs, and data systems using TypeScript, Rust, Python, and Scala. Improve observability, automation, CI/CD, IAM, feature flags, and security; enable self-serve developer workflows; collaborate with SRE, data, QA, and customers; participate in on-call rotation.

As a Data Platform Engineer, you'll architect and implement data services, build data pipelines, optimize for real-time risk assessment, ensure data governance, and collaborate across teams.

Lead design, build, automation, and modernization of enterprise virtualization and hosting platforms (VMware, OpenShift). Drive lifecycle management, capacity, VM provisioning, incident reviews, IaC and automation, vendor collaboration, and platform reliability across the environment.

Lead design, build, automation, and modernization of enterprise virtualization and hosting platforms (VMware and OpenShift Virtualization). Drive platform lifecycle, VM provisioning, performance/capacity, compliance, and incident postmortems. Collaborate with teams and vendors, write and maintain infrastructure code and automation, and participate in Agile delivery while guiding technical decisions, risk management, and mentoring engineers.

Senior individual contributor who defines enterprise AI architecture, builds reusable platform components and governance controls, drives observability and cost attribution, mentors engineers, and partners across Security, Legal, Data, and product teams to enable secure, measurable AI adoption.

The Site Reliability Engineer will automate tasks, enhance platform infrastructure, improve observability, and lead incident response efforts for optimal performance.

As a Software Engineer on the Compute team, you will build and optimize systems for data processing, SQL querying, and improve the performance of code execution. You will tackle complex engineering challenges spanning backend data systems to frontend applications, ensuring efficiency and usability.

As a Staff Software Engineer, you will enhance platform engineering by improving deployment strategies and developer experiences, establishing reliable systems and mentorship across teams.

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 maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Design and deliver blockchain network infrastructure and platform APIs, lead multi-quarter initiatives (chain integrations, re-architecture, data migrations), define APIs/SLOs/observability and on-call ownership, ship SDKs and platform primitives, establish data contracts, and simplify cross-chain operations to enable analytics, ML, and data engineering teams.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.