# Forward Deployed AI Strategy Lead

**Company:** [Prime Intellect](https://hotfix.jobs/companies/prime-intellect)
**Location:** San Francisco, CA
**Role:** Solutions Architecture
**Skills:** AI Infrastructure, Post-Training, Reinforcement Learning, Supervised Fine-Tuning, Evaluations, AI Agents, LangGraph, Dspy, Stagehand, Browserbase, Model Context Protocol, Solutions Architecture, Product Strategy, Technical Go-To-Market
**Posted:** 2026-07-08

> Leads strategic customer deployments for frontier AI infrastructure, translating complex workflows into evaluations, environments, post-training initiatives, and production systems. The role combines technical customer ownership, applied AI strategy, cross-functional research partnership, and revenue responsibility.

## Job Description

## Responsibilities
- Lead strategic customer deployments from technical discovery through proof of concept, production deployment, expansion, and case study.
- Identify high-value AI workflows involving agents, complex automation, model improvement, inference-cost reduction, domain-specific evaluations, and frontier-scale post-training.
- Translate ambiguous customer problems, artifacts, goals, and constraints into executable scopes covering use cases, success metrics, evaluation design, environments, integrations, milestones, commercial structure, risks, dependencies, and expansion paths.
- Partner with Applied Research to prioritize evaluations, environments, agents, and post-training recipes that advance research and deliver customer outcomes.
- Build repeatable customer-deployment processes, including discovery templates, qualification frameworks, proof-of-concept structures, proposal language, pricing inputs, reference architectures, case studies, technical narratives, and deployment playbooks.
- Own customer relationships and revenue progression across qualification, legal, scoping, proposals, procurement, proofs of concept, deployment, and expansion.
- Create clear follow-ups, coordinate senior stakeholders, and prevent deals from stalling in ambiguity.

## Requirements
- Strong intuition for AI products and workflows.
- Ability to understand technical systems without requiring every detail to be pre-digested.
- Excellent written and verbal communication.
- Comfort working with executives, researchers, engineers, and operators.
- High agency, low ego, and ability to operate without an established playbook.
- Ability to manage multiple complex customer workstreams.
- Strong judgment about valuable deployments and strong commercial instincts.
- Deep curiosity about post-training, agents, evaluations, reinforcement learning, and AI infrastructure.

## Nice-to-haves
- Experience in forward-deployed engineering, technical go-to-market, AI product strategy, applied AI, solutions architecture, early-stage startup operations, AI/infrastructure/devtools product management, ML engineering, applied research, AI engineering, or technical venture/investing.
- Experience with reinforcement learning, supervised fine-tuning, evaluations, agents, Model Context Protocol, LangGraph, DSPy, Stagehand, Browserbase, or tool-use workflows.
- Experience working with enterprise AI teams or frontier AI companies.
- Ability to interpret traces, product documentation, API documentation, or technical specifications and turn them into deployment plans.
- Experience writing proposals, customer memos, technical scopes, or launch narratives.
- Founder or early-stage startup experience.
- Strong network across AI startups, research labs, or enterprise software buyers.

## Compensation and Benefits
- Competitive cash compensation and meaningful equity.
- Flexible work in San Francisco or hybrid-remote.
- Visa sponsorship and relocation support.
- Professional development budget.
- Team off-sites and conference attendance.
- Direct exposure to frontier AI labs, leading AI startups, and enterprise AI teams.

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