Senior Product Manager

Senior Product ManagerSplunk is looking for a Senior Product Manager to join our AI Foundations Team! We are building the foundation models and the pretraining, post-training, evaluation, and inference stack around them that power our entire product portfolio for global customers. In this role, you will help define the roadmap for high-scale AI, focusing on domain-specific foundation models for machine data, agentic protocols that let those models take action, and the orchestration platform that serves them.Meet the TeamThe Splunk AI Foundations Organization builds and adapts the foundation models behind Gen AI and ML based solutions across Cisco and Splunk products. We pretrain and post-train models on machine data metrics, logs, traces, events, and configuration and ship the model registry, evaluation harnesses, and inference services that product teams build on. We introduce new offerings that help customers deploy AI at scale responsibly, keeping security and observability at the center of every model we train, evaluate, and serve.Your ImpactAs a Senior Product Manager for the Splunk AI Foundations Team, you will be the architect of the "AI Engine Room" that powers the future of Splunk and Cisco's joint vision for AI-native digital resilience. You will own the model substrate itself: which model families we pretrain versus adapt versus source from partners, how they are post-trained on proprietary machine data, how their quality is measured, and how they are served inside the latency and cost envelopes enterprise workloads demand for our internal Platform, Security, and Observability portfolios and for our global customers and developer ecosystem.In this role, you will lead the productization of domain-specific foundation models (such as the Cisco Time Series Model), taking them from research checkpoint to a versioned, documented, supported product with published benchmarks and a clear deprecation policy. By building agentic workflow frameworks on top of tool-calling and reasoning models, you will empower a new era of "self-driving" operations where autonomous agents collaborate with humans to investigate and remediate incidents in real time. Your work will bridge the gap between groundbreaking AI research and production-grade enterprise software, ensuring that every model and every inference is performant, scalable, and built on a foundation of Responsible AI and trust.Drive Foundation Model Platform Strategy: Own the roadmap for the model substrate model families and sizes, context length, tokenization for machine data, fine-tuning and adapter APIs, embeddings and retrieval, and the SDKs that internal teams and external developers build on. Make and defend the build / adapt / buy decisions behind each capability.Develop Specialized Models: Oversee the shipping of foundation models optimized for machine data, including zero-shot forecasting, anomaly detection, and log and trace understanding. Prove they outperform general-purpose LLMs and classical baselines through rigorous, reproducible evaluation before they reach GA.Own Model Quality & Evaluation: Define what "good" means for each model: golden datasets, offline benchmarks, human review, and online experiments measuring forecast error, tool-call accuracy, grounding and hallucination rates, and regression gates that every checkpoint must clear before release.Build Connectivity: Standardize AI skills for Cisco and Splunk products using common agentic protocols like MCP, creating universal connectors that expose tools, data, and context to any model in the portfolio.Enable Agentic Workflows: Create the frameworks planning loops, tool schemas, memory, evaluation, and guardrails that let developers build autonomous agents capable of reasoning and planning across complex data environments.Responsible AI & Governance: Advance the AI frontier responsibly by engaging with Compliance, Legal, and Finance on red-teaming and safety evaluations, model cards, training-data provenance and licensing, customer data isolation, and tenant-level privacy while owning the unit economics of inference (cost per token, GPU utilization, cloud margin).GTM & Ecosystem: Partner with Product Marketing and Sales to package these foundational capabilities for external customers, including consumption based pricing and metering, positioning against general purpose model providers, model documentation, and developer onboarding.Customer Advocacy: Engage deeply with customers and design partners including on early checkpoints to develop insights into what is possible, uncover unarticulated needs, and ensure customer success.Minimum QualificationsBachelor's degree plus 12 years of related experience in Product Management with a focus on AI/ML platform products; or Master's degree plus 8 years, or PhD plus 5 yearsExperience in the AI/ML lifecycle, including model fine-tuning (SFT/RLHF), orchestration architectures, and data ingestion pipelines (ETL/ELT).Experience implementing security protocols, compliance frameworks, and guardrails within AI or software development platforms.Experience in building or managing observability tools, tracing, or monitoring systems for distributed systems or ML models.Proficiency in SQL and Python for data analysis and managing large-scale data flows into AI-ready formats.Preferred QualificationsAdvanced degree (Master's or Ph.D.) in a quantitative field or an MBA.Hands-on experience with the end-to-end model development lifecycle: data curation, training and fine-tuning runs, evaluation, serving, drift monitoring, and retraining.Knowledge of MLOps and LLMOps principles within networking or cybersecurity, including GPU capacity planning and deploying models in regulated or air-gapped environments.Experience building AI ecosystems or managing partnerships with hyperscalers and model providers, spanning open-weight and frontier models, licensing, and co-development.