AI Deployment Strategist

About Us:

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.

About the Role

Fireworks AI is looking for an AI Deployment Strategist to be the technical backbone of our relationship with customers building on our fast inference platform. You'll be the connective tissue between customer engineering teams and Fireworks' product, engineering, and applied AI teams — driving successful onboarding, deep technical adoption, and long-term account health for companies running production AI workloads at scale.

This is a hybrid technical/commercial role. You'll need enough depth to debug a customer's inference pipeline, discuss quantization tradeoffs, or advise on model routing and fine-tuning strategy — and enough business judgment to run a QBR, spot expansion opportunity, and manage renewal risk before it becomes a problem. Think of it as "product manager for the customer's problem": you own the why and the what — scoping the business case, defining success criteria, and managing the relationship end-to-end. You're embedded with your accounts from early technical evaluation through production and beyond.

What You'll Do

Deployment Strategy & Value Scoping

Partner with Sales during late-stage deals and early post-sale to map the customer's technical and organizational landscape — who the real stakeholders are, what "success" looks like, and where the risk sits

Quantify the business case for the deployment (cost-to-serve, latency/quality targets, ROI vs. the customer's current approach) and define a scoped pilot with clear milestones and exit criteria

Author the internal scoping brief that AI Field Engineering builds against — you own the "why" and "what," they own the "how"

Onboarding & Technical Deployment

Own the post-sale technical relationship from kickoff through go-live, including model deployment, integration architecture, SSO/security configuration, and performance benchmarking

Partner with AI Field Engineering to deliver the successful transition from PoC to production

Build and execute joint success plans with clear milestones, ownership, and timelines

Trusted Advisor & Adoption

Serve as the primary technical point of contact for a portfolio of strategic accounts, building deep relationships with engineering leaders, ML/platform teams, and power users

Advise customers on model selection, fine-tuning, prompt engineering, latency/cost optimization, and agent architecture as their usage matures

Drive usage against business objectives — not just technical enablement, but measurable outcomes (latency SLAs, cost per token, model quality, uptime)

Troubleshooting & Escalation Management

Act as the technical escalation point for production issues, coordinating with Engineering and Support to drive resolution

Maintain runbooks and playbooks that reduce time-to-resolution across the account portfolio

Voice of the Customer

Synthesize patterns across your accounts and feed them into product and engineering roadmaps

Represent customer priorities in internal planning, particularly around model support, tooling gaps, and platform reliability