Platform Engineer

• Design and implement scalable infrastructure for LLM and GenAI workloads across multi-GPU environments.
• Perform GPU profiling, benchmarking, and performance optimization for distributed training workloads.
• Manage and schedule compute-intensive jobs using Slurm-based clusters and OpenShift/Kubernetes environments.
• Enable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS, etc.).
• Collaborate with cross-functional teams to deploy models in research and production environments.
• Build and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps).
• Develop reusable infrastructure templates using tools like Terraform and Helm.

Architect – Platform Engineer

• Responsible for the architecture, roadmap development, efficient operation of Ansible Automation Platform (AAP)
• Blend deep technical mastery with platform engineering leadership
• Envision, design and evolve the Ansible Platform
• Build reusable automation frameworks
• Integrate automation with other platforms like ServiceNow
• Drive operational excellence through AI‑powered automation
• Enable self‑service, reduce operational toil, modernize infrastructure operations

Senior Ansible Automation – Platform Engineer

• Design, implement, and operate the Ansible Automation Platform (AAP) including controller, execution environments, mesh architecture, and collections strategy.
• Define and maintain the Ansible Platform roadmap, including feature evolution, lifecycle management, scalability planning, and enterprise adoption milestones.
• Establish platform governance: coding standards, role/playbook patterns, collections, testing frameworks, and security guardrails.
• Build and maintain Execution Environments (EEs) optimized for performance, security, and dependency management.
• Lead platform upgrades, migrations, and cross-environment standardization.
• Design enterprise-grade Ansible automation frameworks with reusable roles, collections, and modular playbooks.
• Build automation for provisioning, configuration management, patching, compliance, and cloud infrastructure.
• Integrate Ansible with Terraform, CI/CD pipelines, GitOps workflows, and event-driven automation systems.
• Implement self-service automation capabilities for developers, operations, and business teams.
• Integrate Agentic AI systems to enhance automation workflows, including: AI-driven playbook generation and validation, Automated remediation recommendations, Intelligent change-impact analysis, AI-assisted troubleshooting, and root-cause analysis.
• Design automation patterns that combine AI reasoning with Ansible execution for predictive and autonomous operations.
• Collaborate with AI/ML teams to embed automation signals into AIOps pipelines.

Ansible Automation Engineer

• Lead by example, fostering a culture of accountability, technology and architecture excellence, and a customer-first mindset across the organization.
• Attract, build, lead, and develop a high-performing team of technology leaders with diverse expertise, perspectives, and backgrounds.
• Lead the developer experience function, responsible for maximizing AI-enabled developer productivity, efficiency, and satisfaction by reducing friction across the software development lifecycle for thousands of developers building HP products’ software and platform technology stacks.
• Lead the security function, responsible for making secure software delivery the default path, with security embedded into application software, platforms, pipelines, and engineering workflows.
• Lead the data and AI function, responsible for developing and executing a new vision for delivering trusted data, AI, and ML capabilities that software, platform, and product teams can use safely and repeatedly.
• Lead the infrastructure function, responsible for delivering scalable, cost-efficient, secure-by-default infrastructure primitives for all software and platform engineering teams.
• Lead the reliability function, responsible for defining and leading the reliability strategy and execution for the shared platform services that power HP’s products and digital experiences.
• Partner closely with stakeholders to implement and exemplify a true platform engineering producer-consumer model supported by well-defined, aligned service-level objectives.

Vice President, Platform Engineering

• Administer Oracle E-Business Suite R12.2.x, including installation, cloning, upgrades, online patching, and lifecycle management.
• Manage Oracle Database 19c RAC environments, including backup and recovery, high availability, disaster recovery, and performance tuning.
• Support Oracle Fusion Middleware and WebLogic administration, deployments, monitoring, and performance tuning.
• Execute database, EBS technology stack, middleware, and security patching, including Linux administration and vulnerability remediation for Oracle application and database servers.
• Support cloud or hybrid environments across OCI, AWS, or Azure, including automation, monitoring, and production operations.
• Use approved AI tools to explore enterprise use cases and improve productivity, automation, documentation, and service delivery.

Platform Engineer II

• Leading teams of developers (both Disney Cast Members and external vendors) in major software/data development projects.
• Designing and developing highly scalable, distributed enterprise data platform solutions.
• Design, develop and implement scalable distributed enterprise data platform solutions, focusing on multi-cluster warehousing, data sharing, and optimized storage.
• Write code that establishes and enhances frameworks and design proof of concepts, and review code for correctness, and its impact on software architecture.
• Lead the development of high-performance ETL/ELT pipelines using tools like dbt, Python, Airflow and more to integrate diverse data sources.
• Provide mentorship and direction to more junior software engineers on the team; serve as an advanced resources for other engineers on the team.
• Being the ongoing technical lead for data platform products (post-implementation)—leading engineering support for the production platform, leading ongoing enhancement projects, guiding technical product roadmap
• Partner with Data Science, Analytics, and Product teams to ensure the platform meets evolving business requirements.
• Establish standards for data modeling (Vault, Star Schema), security (RBAC), and data quality frameworks.

Lead Data Platform Engineer

• Design and deliver network infrastructure across DNS, edge, ingress, and service mesh
• Build tooling and abstractions for other engineering teams
• Ensure the network is observable, hardened, and resilient by default
• Work closely with cross-functional partners to understand their needs
• Contribute to a team culture of shared ownership and continuous improvement

Senior Platform Engineer – Network and Edge Services

• Own the Ray ecosystem end-to-end: manage KubeRay on GKE, tune Ray Core Task/Actor scheduling, operate the Plasma distributed object store, and configure Ray Data for GPU-direct streaming from GCS/S3
• Operate distributed training with Ray Train: configure TorchTrainer + DDP/NCCL for multi-node H100 clusters, manage checkpoint lifecycle, implement spot-preemption recovery, and integrate warm-start fine-tuning for retrain pipelines
• Build and operate the LLM inference mesh with Ray Serve: compose vLLM (PagedAttention), SGLang (RadixAttention), and NVIDIA Triton (TensorRT/ONNX) as a unified deployment graph with Plasma zero-copy memory sharing
• Optimise inference performance: configure fractional GPU allocation, enable continuous batching, implement per-engine autoscaling based on request queue depth, and tune KV-cache block sizes
• Design and operate the model routing layer: capability-based, version-based, and tenant-based routing with cost-aware fallback between self-hosted SLMs and cloud LLMs
• Build RL training infrastructure: define Flyte workflows for RL pipelines (rollout, reward shaping, policy update, evaluation), integrate Ray RLlib or custom PPO/GRPO loops with Ray Train, and manage replay buffer persistence on GCS
• Operate the full model promotion lifecycle: quality gate → integration tests → load tests (k6) → shadow mode → A/B gate → canary (10%→100%) with golden-signal auto-rollback
• Operate the retrain pipeline: drift detection triggers, warm-start retraining, relative quality gates (V2 >= V1 − 2%), and automated Flyte DAG through to canary
• Integrate RAG retrieval into the inference mesh: vector similarity search, context assembly, and prompt construction before LLM inference

AI Platform Engineer, Training and Inference

• Collaborate with data platform architects to design and specify platform tooling, services, and integrations
• Build data lake and lake house architectures
• Build streaming data architectures to support data platform tooling and services
• Collaborate with software teams to integrate streaming and batch architectures with software products
• Collaborate with data scientists, analysts, and business stakeholders to MVP optimal solutions
• Collaborate with data governance teams and data platform architects to build governance into technical solutions
• Maintain systems and services that provide transparency and observability into our critical systems
• Implement and promote engineering and architectural patterns, perform code reviews, and collaborate in architectural reviews
• Collaborate with product team data engineers and architects to MVP data products, maintain data models, and promote data modeling best practice
• Provide technical leadership and mentorship to product and data engineers, guiding their growth and professional development and enabling their ability to use platform tooling and services
• Lead by example through hands-on contributions to designing, coding, and troubleshooting complex data systems
• Identify and address performance bottlenecks and optimization opportunities within data pipelines, databases, and processing frameworks
• Optimize data processing workflows to improve efficiency and reduce latency
• Lead efforts to diagnose and resolve data-related incidents in a timely manner
• Will participate in the grooming of stories
• Will be responsible for their own tasking towards the completion of stories
• Will mentor junior members of the team and guide junior members on best practice
• Will participate in design
• Will be responsible for the quality of their own code and will participate in code review of others product
• Will be responsible for integrating their own code with the team's DevOps plan and implementation

Data Platform Engineer

• Serve as a key engineer for the GenAI platform and contribute technical expertise to teams in closely aligned technical areas such as AIML Platform, DevOps, Compute and Cloud.
• Co-design major software components of the GenAI Platform and contribute to development of production code in Python and participate in both design reviews and PR reviews.
• Accountable for key component(s) of GenAI Platform with particular focus on usability, reproducibility and performance at scale
• Integrate with DataOps, and Data Engineering products for best performance and ease of use in ML training at scale
• Participate in scrum teams and contribute technical expertise to teams in closely aligned technical areas.
• Able to design innovative strategies and ways of working to create a better environment for the end users
• Standard bearers for proper ways of working and engineering discipline, including CI/CD best practices and proactively contribute to improvements within your engineering area.

Senior Gen AI Platform Engineer

• Architect and evolve our cloud-native platform and services to support high-throughput, low-latency data processing patterns, customer-facing features, and design platform to meet scalability requirements.
• Design scalable, distributed systems powering complex capabilities such as authentication & authorization, data lifecycle management, metadata management, operational intelligence, and real-time event processing.
• Evolve the authorization service toward modern identity standards and customer-configurable, fine-grained access models that scale without a release for every new role including authorization for non-human identities (service-to-service, AI agents, MCP-based tooling).
• Built systems that capture and enforce structured metadata at ingest and serve it through clean service contracts; understands where platform metadata plumbing ends and the semantic/ontology layer begins, and collaborates well across that boundary.
• Build governance primitives for a regulated environment — compliance-grade audit trail, dataset-level access controls, and approval / eSignature workflows.
• Collaborate with engineering and product teams to deliver infrastructure that supports new services, customer-facing applications, and high-volume data processing workloads.
• Build and maintain infrastructure-as-code (e.g., CloudFormation, AWS CDK) to automate, standardize, and secure deployments to support online upgrades and on-demand infrastructure allocation.
• Enhance observability and monitoring to ensure reliability, cost efficiency, and rapid incident response.
• Champion best practices in distributed systems design, scalability, and performance optimization, and share architectural insights through design reviews and technical documentation.

Lead Software Platform Engineer

• Own our entire platform engineering and SRE function using autonomous agents, LLM-powered pipelines, and MCP-based tooling
• Inherit a mature, fully containerized AWS estate (9 EKS clusters, 27 accounts, 228 Kubernetes nodes)
• Manage an Akamai CDN layer handling live traffic splits
• Oversee GitHub Actions + Jenkins CI/CD pipelines for a Webpack 5 micro-frontend monorepo
• Extend automation across AWS infrastructure, incident management, and deployment processes
• Build and improve autonomous agents for monitoring, alerting, triage, and routine operational work
• Own AWS infrastructure, CI/CD release management, CDN management, security, monitoring, and incident response

Staff AI Platform Engineer

• Design, deploy, and operate AKS clusters for production application workloads
• Own Azure infrastructure provisioning through Terraform (IaC)
• Build and operate GitOps-based deployment pipelines using ArgoCD
• Operate observability stack (Prometheus, Grafana, Loki)
• Participate in the 24x7 on-call rotation for platform services
• Respond to incidents promptly and lead root cause analysis
• Implement security controls in Azure appropriate for a regulated enterprise environment
• Document operational runbooks, decision records, and platform standards
• Identify and reduce toil through automation

Platform Engineer – III

• Accelerate software delivery by designing and building internal tools and services that reduce friction for engineering teams.
• Own and evolve our CI/CD infrastructure, not just maintain it, but rethink how it should work as we scale
• Build and maintain paved paths: reusable patterns and templates that make the right defaults obvious and easy to follow
• Build release engineering tooling that gives engineers confidence and control over how their code ships
• Identify bottlenecks in developer workflows through close collaboration with engineering teams, then build lasting solutions, not workarounds
• Contribute to the architectural decisions that will shape how 200+ engineers build and ship software and images
• Help shape how the Platform Engineering team itself operates: our processes, priorities, and our push toward a self-service engineering org
• Participate in an on-call rotation and keep the systems you own well-documented, reliable, and observable

Senior Platform Engineer

• Design and deploy modern data platforms in Azure Databricks, including full environment setup (storage, compute, catalogs, security) and infrastructure optimization
• Build scalable data pipelines and Lakehouse solutions using Python, SQL, and orchestration across Databricks and Microsoft Fabric
• Lead customer implementations and technical delivery, supporting new deployments, upgrades, and escalation troubleshooting in Azure environments
• Develop and manage ETL/ELT integrations across third-party tools and custom APIs, ensuring reliable, production-grade data workflows
• Implement secure, high-performance data architectures, including Unity Catalog, OneLake security, networking (VPNs, private endpoints), and cost optimization
• Partner directly with customers as a technical advisor, translating business needs into data platform solutions and driving best practices across Databricks and Fabric

• Lead complex initiatives to develop infrastructure to provide solutions for business applications
• Participate in various projects intended to continually improve or upgrade the infrastructure
• Evaluate internal and external software solutions which could be leveraged to meet target state architecture goals
• Review and analyze high impact outages to ensure the proper processes and procedures are in place to avoid problems in the future
• Design, build, deploy and maintain infrastructure solutions through collaborative efforts with the team and third party vendors
• Design, code, test, debug and document programs using Agile development practices
• Make decisions in technical designs, implementation plans and identify project risks and resource requirements
• Direct the daily risk and control flow of operations, focusing on policies, procedures and work standards to ensure success
• Recommend courses of action to maintain cost effectiveness and achieve results
• Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals
• Interact with customer and vendor

Lead Infrastructure Platform Support Engineer

• Own, operate, and improve Cursor’s Databricks and lakehouse infrastructure.
• Build and optimize ingestion systems for first-party product data and 3rd-party business systems.
• Ensure observability, alerting, and operational standards across all data infrastructure layers.
• Evaluate and roll out data tooling where it solves real stakeholder needs.
• Partner with technical and non-technical partners to understand recurring data problems and turn them into scalable platform solutions.

Analytics Platform Engineer

• Lead the engineering organization for data and AI/ML platform
• Develop petabyte-scale infrastructure for data and AI
• Drive cross-functional alignment and strategic platform decisions
• Set product vision and roadmap for platform areas
• Manage engineering team and grow organization

Director, Data & AI/ML Platform Engineering

• Design, build, and operate the enterprise Snowflake data platform supporting multiple business domains (Finance, HR, Supply Chain)
• Own the Snowflake platform lifecycle , including account topology, RBAC, network architecture, governance, CI/CD automation, and disaster recovery
• Build and maintain multi‑account Snowflake environments (PROD / NONPROD / DEV) on Snowflake Business Critical edition
• Implement secure Azure Private Link connectivity , including private endpoints, Private DNS Zones, and network policy enforcement
• Develop and maintain Terraform modules to automate provisioning of Snowflake resources (databases, schemas, warehouses, roles, tags, monitors, integrations)
• Enable self‑service platform consumption through validated YAML manifests deployed via Shield CI/CD pipelines
• Integrate Snowflake with Entra ID (Azure AD) using SAML SSO, SCIM provisioning, and enterprise RBAC patterns
• Manage service account authentication, secrets, and key rotation using HashiCorp Vault and Azure Key Vault
• Enforce security and compliance controls , including data masking, row‑level security, object tagging, audit logging, and policy‑as‑code
• Embed infrastructure and supply‑chain security scanning into CI/CD pipelines (Checkov, tfsec, Semgrep, Snyk)
• Design for resiliency and cost efficiency , including warehouse sizing, resource monitors, cost allocation tags, and chargeback reporting
• Own cross‑region disaster recovery , replication strategies, and RPO/RTO adherence
• Govern secure data sharing patterns and monitor data sharing usage and approvals
• Partner with Cloud, IAM, Security, and FinOps teams to ensure alignment with enterprise standards and regulatory requirements

Lead Platform Engineer

• Contribute to the implementation and evolution of foundational SDKs and libraries used across Adobe products, supporting consistent telemetry, analytics, and AI-powered services.
• Apply computer science principles including algorithms, data structures, concurrency, memory management, and system architecture to real-world production systems.
• Use AI tools such as Claude Code, Cursor, or the latest for goal-focused development.
• Concentrate on expressing concepts, developing solutions, constraints, and outcomes, and validating results.
• Develop and maintain reference implementations and documentation that help product teams successfully adopt shared platform components.

See all jobs on JobTailor

Architect – Platform Engineer

Senior Ansible Automation – Platform Engineer

Ansible Automation Engineer

Vice President, Platform Engineering

Platform Engineer II

Lead Data Platform Engineer

Senior Platform Engineer – Network and Edge Services

AI Platform Engineer, Training and Inference

Data Platform Engineer

Senior Gen AI Platform Engineer

Lead Software Platform Engineer

Staff AI Platform Engineer

Platform Engineer – III

Senior Platform Engineer

Data Platform Engineer

Lead Infrastructure Platform Support Engineer

Analytics Platform Engineer

Director, Data & AI/ML Platform Engineering

Lead Platform Engineer

Senior Software Development Engineer, SDK – Cloud Data Platform Engineering