Technical Account Manager, Enterprise

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

Requirements

• 4+ years in a technical, customer-facing role: 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
• 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

Nice-to-Haves

• Experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, etc.)