Q

Qualcomm

Designs mobile chips and licenses wireless patents

AI Platforms Leader - Software Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSan Diego, CA, USA

About the Job

Requirements

  • At least 15 years of overall engineering or technology experience, including approximately 10 years building and operating large-scale AI/ML, data, or high-performance computing platforms.
  • At least five years leading a team of approximately 10 engineers across platform, site reliability engineering, MLOps, and LLMOps, including coaching, hiring, performance management, and execution rhythms.
  • Hands-on experience operating on-premises GPU clusters using Kubernetes with a GPU operator and/or Slurm, including scheduling, capacity planning, performance tuning, and reliability.
  • Strong experience with model lifecycle processes, model and agent evaluation, safety guardrails, and observability.
  • Deep experience with AWS, GCP, or Azure AI/ML services and managed Kubernetes, networking, security, identity, and cost management.
  • Experience with CI/CD, GitOps, infrastructure as code using Terraform, Bicep, or Helm, containerization with Docker, Kubernetes, and secure software development lifecycle practices.
  • Understanding of agent orchestration, A2A patterns, tool abstractions, and operating MCP servers in production.
  • Demonstrated success running AI or computing clusters with service-level objectives, on-call operations, incident management, and post-mortems.
  • Experience leading a distributed engineering team across time zones.
  • A bachelor's degree in Engineering, Computer Science, or a related field is required; alternatively, the minimum qualifications accept a bachelor's degree and 8+ years, a master's degree and 7+ years, or a PhD and 6+ years in Engineering, Information Systems, Computer Science, or a related field.
  • At least four years of work experience with a programming language such as C, C++, Java, or Python.

Responsibilities

  • Own the AI platform strategy and roadmap, defining a multi-year vision for a multi-tenant hybrid on-premises and cloud platform aligned with business needs, developer productivity, and cost efficiency.
  • Establish platform service-level agreements and objectives, reliability goals, and security and compliance guardrails.
  • Operate and optimize on-premises GPU clusters, including capacity planning, scheduling, partitioning, NCCL, and high-throughput storage and networking.
  • Drive GPU utilization efficiency, right-sizing, and cost transparency across training and inference workloads.
  • Deliver MLOps and LLMOps platform capabilities for data preparation, training and fine-tuning, model registry, lineage, governance, evaluation, red-teaming, and safe batch, online, and streaming deployment.
  • Implement CI/CD for models, prompts, and agents; automate evaluations and rollout or rollback using canary, A/B, and shadow deployments.
  • Lead the design and operation of agentic orchestration using A2A patterns, tool integration, and MCP servers to securely expose enterprise tools and data.
  • Standardize agent capability schemas, guardrails, observability, and policy enforcement.
  • Use AWS and Azure AI services for training and inference, including Bedrock, SageMaker, EKS, Azure AI Studio, Azure ML, AKS, and Azure OpenAI, with networking, identity, secrets, and cost controls.
  • Establish multi-cloud patterns for portability, resilience, and vendor risk management.
  • Own platform services for identity and role-based access control, secrets, service meshes, logs, metrics and traces, data access controls, vector stores, feature stores, and model gateways such as KServe, Triton, and vLLM.
  • Use GitOps and infrastructure as code with Terraform, Bicep, and Helm, and secure software supply chain practices including software bills of materials, image signing, and policy as code.
  • Lead an approximately 10-engineer global platform, site reliability, MLOps, and LLMOps team, supporting global collaboration, 24/7 readiness, and a healthy on-call rotation.
  • Drive incident response, post-mortems, and continuous improvement, partnering with Security, Legal, and Compliance on model and data governance.
  • Partner with product, data, and application teams to enable high-impact AI use cases.
  • Manage strategic cloud, GPU, and enterprise AI tooling vendors, and negotiate licenses and statements of work aligned to the roadmap and budget.

Desired Qualifications

  • Experience with PyTorch, CUDA, cuDNN, Triton Inference Server, vLLM, KServe, Ray, and Slurm.
  • Experience with high-throughput storage such as Lustre, BeeGFS, or Ceph; vector databases such as FAISS, Milvus, Pinecone, or Azure AI Search; and feature stores such as Feast.
  • Experience with MLOps toolchains including MLflow, Vertex, Azure ML, SageMaker registries, Airflow, Argo, Weights & Biases, LangSmith, and prompt or version management.
  • Experience with OIDC, role-based access control, policy as code using OPA, secrets management with AWS Secrets Manager or Azure Key Vault, model governance and risk controls, and privacy and personally identifiable information safeguards.
  • Experience with agentic frameworks such as Semantic Kernel, LangChain, CrewAI, or AutoGen, and integrating enterprise tools through MCP.
  • A master's or PhD in Computer Science, Electrical Engineering, Mathematics, or a related field.
  • A track record of shipping platform capabilities that enable multiple product teams through self-service, documentation, software development kits, templates, and golden paths.
  • Experience communicating with executives and technical leaders using clear metrics, dashboards, and business-value storytelling.

About the Company

cb

Qualcomm

Designs mobile chips and licenses wireless patents

Qualcomm designs the Snapdragon processors and modems inside many Android phones, and it licenses its wireless patents to device makers around the world. It helped commercialize CDMA, the cellular technology behind 3G networks, and its patents cover core parts of the 4G and 5G standards that phones use to connect. The company does not run its own chip factories; contract foundries make its chips, which it sells to makers of phones, PCs, cars, and connected devices. Device makers also pay Qualcomm royalties for using its patented technology, which forms a second business alongside chip sales. Qualcomm was founded in 1985 and is based in San Diego, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Diego, CA
Founded
1985

Recently Posted Jobs

Explore more opportunities at Qualcomm

See more jobs