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About This Role
- Remote
- Full\-time
- Client Success
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Powerfront Inc. (www.powerfront.com) isn't just another SaaS provider; we're the architects of the INSIDE™ Ai Customer Visual Engagement Platform. An Ai powered solution used by most luxury brands allowing them to *see* their online world in real\-time. We're talking about live customer behavior tracking and the power to engage at the precise moment of impact. Forget static analytics—we're revolutionizing customer interaction for industry titans like LV, Gucci, Ferrari, Cartier, Valentino, Staples, Rooms To Go and Lenovo.
We’re a global, fully remote team fueled by passion and a shared obsession with cutting\-edge technology. We are fully committed to the future of Ai, with the majority of our development work focused on account Ai and intelligent customer engagement. We believe Ai is the foundation of modern customer experience and are dedicated to building solutions that leverage its full potential.We’re not just building software; we’re forging strategic partnerships that redefine customer engagement.
If you're driven to tackle complex challenges, thrive in a dynamic, collaborative environment, and want to leave your mark on the next generation of e\-commerce, Powerfront is your launchpad. This isn’t just a job; it’s a career\-defining opportunity to be part of a team transforming how the world connects with brands.
We are looking for a highly motivated game\-changer to join our team. This is an amazing opportunity for an individual who loves being on the cutting edge of AI technology.
Key Responsibilities
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- Design, build, and maintain automated AI workflows using Activepieces (or similar platforms like Make, Zapier, or n8n) to bridge AI models with business applications.
- Design, test, and refine prompts for AI models to optimize output quality, moving beyond simple instruction to complex, multi\-step chain\-of\-thought logic.
- Implement and manage LLM observability using tools like Langfuse to monitor traces, evaluate cost/latency, and debug production issues.
- Analyze AI model behavior and performance, using data\-driven insights to improve accuracy and effectiveness.
- Conduct A/B testing on prompt variations and workflow logic to maximize output quality and operational efficiency.
- Create structured documentation and guidelines for best practices in both automation logic and prompt engineering.
- Stay updated on advancements in AI language models and integration ecosystems to ensure the company uses the most efficient tools available.
Key Requirements
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- Proven experience building complex automations using Activepieces, Zapier, Make, or n8n. You should understand how to handle webhooks, APIs, and conditional logic.
- Hands\-on experience with Langfuse (or similar tools like LangSmith or Helicone) for tracking, monitoring, and evaluating LLM application performance.
- Deep experience in crafting and fine\-tuning prompts, including few\-shot prompting, retrieval\-augmented generation (RAG) contexts, and structured output formatting.
- Strong understanding of AI language models (e.g., GPT\-4, Llama 3, Claude 3\.5\) and how they integrate into broader software stacks.
- Proficiency in Python and familiarity with AI/ML frameworks or API\-first development.
- Ability to interpret model outputs and use observability data to systematically optimize performance rather than relying on "trial and error."
- Bachelor's or higher degree in Computer Science, AI, or a related technical field (or equivalent practical experience).
Additional Desired Qualifications
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Summary of Benefits
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- A dynamic \& forward\-thinking organization revolutionizing retail technology
- A virtual workforce, remote but highly interactive and collaborative
- Competitive salary and benefits
- Be a part of an amazing culture with a high client and staff retention
- Take pride in partnering with the most prestigious brands in the world
- Career progression and longevity
Note: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. Assignments may differ for individuals within the same role based on business conditions, departmental need or geographic location.
Powerfront provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics.
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 Powerfront Inc, 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.
Powerfront Inc AI Hiring
Powerfront Inc has 1 open AI role right now. They're hiring across Prompt Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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
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