forward deployed engineers
- AI Strategy (315)
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How to Build a Career as a Forward-Deployed AI Engineer
A practical guide to assessing your fit, reading ambiguous job descriptions, and building evidence for a forward-deployed AI engineering role.
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Enterprise AI Spend Is Concentrating. Manage It as a Portfolio
A practical operating model for separating AI activity from value, attributing costs correctly, and scaling proven workflows without rewarding waste.
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How to Run Customer-Facing AI Implementations That Scale
A practical operating model for turning vague customer AI requests into safe production deployments without creating a permanent custom-work queue.
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Why Product Engineers Are Transforming Software Delivery: Ownership, Speed, and Real Impact
Product engineers blend coding with customer insight, owning problems end to end and eliminating costly handoffs. By pairing continuous discovery with CI/CD, they shorten feedback loops…
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Old-School Selling Beats PLG in the AI Era: My GTM Playbook for 8‑Day Enterprise Deals
Old-school, in-person selling still beats product-led motions in the AI era because enterprise buyers reward teams who show up and de-risk change. I share how to…
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From Internal FinOps Agents to Customer-Embedded Optimization
Learn how to turn internal FinOps agents into governed, customer-embedded workflows that improve cloud cost-to-value and product learning.
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AI-Native Startup Execution: A Practical Operating System
A practical operating model for choosing the wedge, focusing the ICP, designing the team, and turning model-quality evidence into customer outcomes.
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Cracking the Hardest Percentages: Turn Complex Support into Scalable, Trust-Building Automation
Complex queries are the smallest slice of most queues, yet they dominate handle time and define customer trust. In my experience, the fastest path to higher…
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Goal-Setting for AI Products: How I Plan, Prioritize, and Confidently Ship in a Nonlinear GenAI World
Planning AI roadmaps is fundamentally different from traditional software. I share how I set outcomes-first OKRs, balance research and product development, and scale safely without over-planning.…
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An Operating System for AI-Era Product and Engineering Leaders
A practical model for turning faster AI-assisted output into reliable customer outcomes through clear bets, evals, decision rights, and learning loops.
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Inside Alyx: Dogfooding, Evals, and Observability That Power an Agentic AI Future
This is a pragmatic, first-hand look at how Alyx was built by dogfooding Arize’s own platform—moving from messy notebooks to a disciplined eval and observability stack.…
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How Braintrust Nailed Product-Market Fit: Paranoia, Patience, and High-Bar Quality
Braintrust’s playbook for product-market fit in GenAI pairs a relentless quality bar with deliberate go-to-market restraint. By transforming evaluation pain into an end-to-end platform for building…
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The AI Support Blueprint: From Zero Playbook to 75% Resolution and a Reimagined Team
Rolling out an AI Agent doesn’t just change workflows—it reshapes the team itself. I share how we went from no playbook to a functional AI Support…
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A Practical Operating Model for GenAI Product Discovery
A practical operating model for linking customer evidence, GenAI evaluations, field pilots, decision rights, and production learning without slowing teams.
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Building an AI-Era Product Operating Model That Can Learn
A practical operating model for giving AI product teams clear outcomes, faster evidence loops, proportionate governance, and a repeatable path to scale.
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