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.

What You’ll Do

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
• Track a tight feedback loop between what customers are building and what Fireworks ships next

Expansion, Renewal & Business Reviews

• Own the technical narrative for quarterly/executive business reviews (QBRs/EBRs), including adoption trends, ROI, and roadmap alignment
• Partner with the Account Executive on renewal strategy and identify expansion opportunities tied to new use cases, teams, or workloads
• Forecast and proactively flag account health risks before they threaten retention

What We’re Looking For

• 4+ years in a technical, customer-facing role: Deployment Strategist, 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 enough hands-on coding ability to debug integrations, not just describe them
• 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 fluently
• Comfortable owning ambiguity in a fast-moving, early-stage function; you’ll be shaping the TAM playbook, not just following one
• Bonus: experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, et…