AI Deployment Strategist

Fireworks AI; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Fireworks AI seeks a Deployment Strategist to own the technical and commercial relationship with a portfolio of strategic accounts. You will scope business cases, define success metrics, and manage the customer journey from initial evaluation through production and expansion.

You will partner with Sales on late-stage deals and post-sale delivery, map stakeholders, quantify ROI, and design pilots with clear milestones.

Qualifications

• Deployment strategies for AI at scale in enterprise environments.
• Experience scoping high-value engagements with pilots and PRDs.
• Strong communication linking ML engineers and business stakeholders.

Responsibilities

• Own the post-sale technical relationship from kickoff through go-live, including model deployment and integration architecture.
• Partner with AI Field Engineering to transition from PoC to production.
• Build and execute joint success plans with milestones and ownership.
• Be 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 usage against business objectives with measurable outcomes (latency, cost, uptime).
• Troubleshoot and escalate production issues.
• Synthesize patterns across accounts for product roadmaps and customer feedback.
• Own technical narrative for quarterly business reviews (QBRs) and renewal strategies.

Skills

Deployment Strategy

Technical Account Management

Solutions Engineering

Forward-Deployed Engineering

Technical Customer Success

Model deployment

Prompt engineering

Fine-tuning

RAG

Agent architectures

APIs

Cloud infrastructure

Coding ability

Value-based ROI framing

Pilot scoping

Tools

AWS/GCP/Azure

Open-source model ecosystems

Job description

• 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
• 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
• 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

Comfortable owning ambiguity in a fast-moving, early-stage function; you’ll be shaping the playbook for this role at Fireworks, not just following one4+ 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 companyDirect experience with LLMs in production — understanding of probabilistic model behavior, prompt engineering, fine-tuning, RAG, and/or agent architecturesBonus: experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, etc.)Strong technical foundation: comfortable with APIs, cloud infrastructure (AWS/GCP/Azure), and enough hands-on coding ability to debug integrations, not just describe themExperience scoping high-value engagements — value-based/TCO-ROI framing, defining pilot success criteria, writing scoping docs or PRDs — gained in a Deployment Strategist, Forward-Deployed Engineer, or similar “product manager for the field” capacity is a strong plusExcellent written and verbal communication — able to translate between ML engineers and business stakeholders fluentlyTrack record of managing enterprise relationships end-to-end: technical delivery, executive communication, and commercial outcomes (renewal/expansion)