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Fireworks is a Series D AI infrastructure company ($17.5B valuation) backed by NVIDIA, Sequoia, Benchmark, and others. The platform enables enterprises to build, train, and serve specialized AI models tailored to their data and workflows, supporting hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads.
You will serve as the AI Deployment Strategist—the technical backbone of customer relationships for companies running production AI at scale. This hybrid technical/commercial role bridges customer engineering teams with Fireworks' product, engineering, and applied AI teams. You'll own the full customer lifecycle from late-stage sales through production deployment and renewal, acting as a "product manager for the customer's problem."
Key Responsibilities:
**Deployment Strategy & Value Scoping**: Partner with Sales during late-stage deals to map the customer's technical and organizational landscape. Quantify the business case (cost-to-serve, latency/quality targets, ROI) and define scoped pilots with clear milestones. Author internal scoping briefs that guide AI Field Engineering's execution.
**Onboarding & Technical Deployment**: Own the post-sale technical relationship from kickoff through go-live, including model deployment, integration architecture, SSO/security, and performance benchmarking. Partner with AI Field Engineering to transition from PoC to production and build joint success plans.
**Trusted Advisor & Adoption**: Serve as the primary technical contact for a portfolio of strategic accounts. Advise on model selection, fine-tuning, prompt engineering, latency/cost optimization, and agent architecture. Drive measurable outcomes (latency SLAs, cost per token, model quality, uptime).
**Troubleshooting & Escalation**: Act as the technical escalation point for production issues, coordinating with Engineering and Support. Maintain runbooks and playbooks to reduce time-to-resolution.
**Voice of the Customer**: Synthesize patterns across accounts and feed them into product and engineering roadmaps. Represent customer priorities in internal planning around model support, tooling, and platform reliability.
**Expansion, Renewal & Business Reviews**: Own the technical narrative for QBRs/EBRs, including adoption trends, ROI, and roadmap alignment. Partner with Account Executives on renewal strategy and identify expansion opportunities. Proactively flag account health risks.
Requirements:
- Bachelor's degree required
- 4+ years in a technical, customer-facing role: Deployment Strategy, Technical Account Management, Solutions Engineering, Forward-Deployed Engineering, or Technical Customer Success at an enterprise software or AI/ML company
- Strong technical foundation: comfortable with APIs, cloud infrastructure (AWS/GCP/Azure), and hands-on coding ability to debug integrations
- Direct experience with LLMs in production—understanding of probabilistic model behavior, prompt engineering, fine-tuning, RAG, and/or agent architectures
- Track record of managing enterprise relationships end-to-end: technical delivery, executive communication, and commercial outcomes (renewal/expansion)
- Excellent written and verbal communication—able to translate between ML engineers and business stakeholders
- Comfortable owning ambiguity in a fast-moving, early-stage function; you'll shape the playbook for this role
- Experience scoping high-value engagements (value-based/TCO-ROI framing, defining pilot success criteria, writing scoping docs or PRDs) gained in a Deployment Strategist or Forward-Deployed Engineer capacity is a strong plus
- Bonus: experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, etc.)
