Interested in this Prompt Engineer role at carnaby fox?
Apply Now →Skills & Technologies
About This Role
Hiring: Founding Deployment Strategist (AI Customer Success \& Prompt Engineering)
Location: San Francisco, California (On\-site)
Employment Type: Full\-Time
Compensation: $140,000\+Base with Competitive Founding Equity
Visa Sponsorship: Not Available (H1B Transfers Welcome)
Join a High\-Growth, Venture\-Backed AI Startup
We're partnering with a rapidly growing, venture\-backed AI startup that's transforming how B2B companies engage, educate, and convert customers through next\-generation conversational AI.
Backed by $6M in funding and having achieved 100\+ customers in under 60 days, our client is entering an exciting phase of growth and is looking for an exceptional Founding Deployment Strategist to join its early team.
This is not a traditional Customer Success or Solutions Consultant position.
You'll own the complete customer deployment lifecycle—from understanding each customer's business and sales process to designing, deploying, optimizing, and continuously improving AI agents that become an extension of their revenue teams.
Working directly with founders, enterprise customers, and product leadership, you'll help shape the future of AI\-powered customer engagement while influencing both customer outcomes and product strategy.
Why This Opportunity?
✔ Join a rapidly scaling, venture\-backed AI startup.
✔ Become an early founding team member with meaningful equity.
✔ Work directly with founders and senior leadership.
✔ Own the complete customer onboarding and deployment strategy.
✔ Influence product development through direct customer insights.
✔ Build AI\-powered customer experiences that create measurable business impact.
✔ Work at the intersection of Artificial Intelligence, Prompt Engineering, Customer Success, Product Strategy, and Go\-to\-Market execution.
✔ Accelerate your career while helping define how businesses adopt conversational AI.
Key Responsibilities
As the Founding Deployment Strategist, you will:
- Own the complete deployment journey from customer sign\-up through successful go\-live.
- Understand each customer's products, business model, buyer personas, positioning, and sales strategy.
- Configure and optimize AI agents using Prompt Engineering and conversational workflows.
- Monitor live AI conversations, identify trends, and continuously improve agent performance through prompt tuning and evaluation.
- Act as the primary customer contact via Slack Connect while delivering an exceptional onboarding experience.
- Translate customer insights into product improvements by working closely with Engineering and Product teams.
- Build scalable deployment frameworks that support rapid customer growth.
- Partner cross\-functionally with Product, Engineering, Leadership, and Customer Success teams to maximize customer outcomes.
Required Qualifications
We're looking for professionals who combine strategic thinking, customer engagement, and AI expertise.
Experience
- 1–3 years of experience at McKinsey, Bain, or BCG (MBB)
- Experience working with enterprise customers and executive stakeholders.
- Proven ownership of customer\-facing strategic initiatives.
Technical Skills
- Artificial Intelligence (AI) Tools
- Prompt Engineering
- AI Agents
- Evaluation (Eval) Systems
- Systems Thinking
- Enterprise Software
- Customer Deployment
- Excellent communication and stakeholder management skills.
Ideal Candidate Profile
You'll thrive in this role if you:
✔ Love solving customer problems.
✔ Enjoy working directly with founders and executive stakeholders.
✔ Think in systems rather than isolated tasks.
✔ Learn new products quickly.
✔ Have outstanding communication and relationship\-building skills.
✔ Are passionate about AI and emerging technologies.
✔ Thrive in fast\-paced startup environments.
✔ Take ownership and execute with minimal direction.
✔ Combine consulting\-level problem\-solving with startup speed.
Preferred Background
Candidates with experience in any of the following areas are highly encouraged to apply:
- Management Consulting
- Artificial Intelligence
- B2B SaaS
- Customer Success
- Product Strategy
- Solutions Engineering
- Revenue Operations
- Go\-to\-Market Strategy
- Enterprise Software
Hands\-on experience with conversational AI, prompt engineering, AI workflows, or customer\-facing AI products is highly desirable.
Preferred Company Experience
We're particularly interested in candidates who have experience at:
- McKinsey \& Company
- Bain \& Company
- Boston Consulting Group (BCG)
along with experience at high\-growth venture\-backed startups where you've owned strategic customer initiatives and worked directly with founders or senior leadership.
Education
- Bachelor's degree required.
- Strong analytical thinking, structured problem\-solving, and exceptional communication skills are essential.
What Success Looks Like
In this role, you'll help customers successfully deploy AI\-powered sales agents that create measurable business impact.
You'll transform complex products into intelligent conversational experiences, continuously optimize AI performance, and serve as the critical bridge between customers and product development.
Your work will directly influence customer success, product evolution, and the continued growth of one of Silicon Valley's most exciting AI startups.
Ready to Build the Future of Conversational AI?
If you're excited about combining strategy, AI, customer success, and startup execution while working alongside world\-class founders, we'd love to hear from you.
Pay: From $140,000\.00 per year
Application Question(s):
- As we are specifically looking for (MBB), Do you have 1–3 years of experience at McKinsey, Bain, or BCG (MBB)? (Mandatory)\*
- What is your current compensation (Per Anum) ? Mandatory\*
- What is your expected compensation (Per Anum) ? Mandatory\*
- If you didn't mention your Linkedin in resume, Please post link below. (Mandatory)\*
Education:
- Bachelor's (Required)
Work Location: In person
Role Details
About This Role
Prompt Engineers design, test, and optimize interactions with large language models. They build evaluation frameworks, craft system prompts, and develop techniques like chain-of-thought and few-shot learning to get consistent, reliable outputs. The role emerged alongside the GPT-3 era and has matured into a legitimate engineering discipline, not the 'just talk to the AI' job that early skeptics dismissed.
The work is more systematic than creative. You're running hundreds of prompt variations through evaluation suites, measuring output quality across edge cases, and building guardrails for production systems. When a prompt works 95% of the time but fails catastrophically on the other 5%, you need to find those failure modes and fix them before they hit users.
Across the 3,708 AI roles we're tracking, Prompt Engineer positions make up 0% of the market. At carnaby fox, this role fits into their broader AI and engineering organization.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
What the Work Looks Like
A typical week involves designing evaluation datasets for new use cases, benchmarking prompt strategies against each other with statistical rigor, working with product teams to define 'good enough' output quality, and building the tooling that lets non-technical teammates iterate on prompts safely. You'll spend more time in spreadsheets and evaluation dashboards than you'd expect.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
Skills Required
The core requirement is deep LLM experience: prompt design, RAG architectures, and evaluation methodology. Python is table stakes. Many roles also want experience with specific providers like OpenAI, Anthropic, or open-source models. Understanding tokenization, context windows, and the practical differences between model families (reasoning ability, instruction following, output format compliance) separates strong candidates from the crowd.
Evaluation skills are becoming the differentiator. Can you design a rubric that measures output quality? Can you build automated evaluation pipelines? Do you understand when to use human evaluation vs. LLM-as-judge vs. deterministic checks? Companies are moving past 'vibes-based' prompt testing and want engineers who bring measurement discipline.
Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
Compensation Benchmarks
Prompt Engineer roles pay a median of $140,000 based on 11 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
carnaby fox AI Hiring
carnaby fox has 2 open AI roles right now. They're hiring across Prompt Engineer, AI Agent Developer. Positions span San Francisco, CA, US, Margaretville, NY, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into Prompt Engineer roles include Technical Writer, NLP Researcher, Software Engineer.
From here, career progression typically leads toward AI Product Manager, LLM Engineer, AI Solutions Architect.
The best prompt engineers come from technical backgrounds and add LLM expertise, not the other way around. If you're coming from a non-technical role, invest heavily in Python, evaluation methodology, and understanding how LLMs work under the hood (tokenization, attention, context windows). The role will increasingly merge with LLM Engineering as the tools mature.
What to Expect in Interviews
Interviews focus on evaluation methodology and systematic thinking. You'll likely be asked to design a prompt for a specific use case, explain how you'd measure output quality, and walk through how you'd debug a prompt that works 90% of the time but fails on edge cases. Expect to discuss tokenization, context window management, and the tradeoffs between different prompting strategies (few-shot vs. chain-of-thought vs. tool use).
When evaluating opportunities: Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.