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About This Role
- Remote
- Full\-time
- Engineering
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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.
Key Responsibilities
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- AI Model Development \& Integration: Design, develop, and integrate AI models, primarily utilizing GPT and other large language models (LLMs), into our core SaaS platform. This includes implementing features such as intelligent chatbots, automated ticket routing, sentiment analysis, personalized recommendations and proactive customer support.
- Tool Calling Implementation: Develop and integrate AI solutions utilizing Tool Calling functionalities within GPT integrations. This will involve connecting our AI models to external tools and APIs to enhance their capabilities and enable more complex interactions.
- Data Management \& Analysis: Work with large datasets related to customer interactions, product information, and retail transactions. Leverage your data analysis skills to identify trends, patterns, and insights that inform AI model training and optimization. A strong understanding of retail customer journeys and common customer service issues within the retail industry is crucial.
- Database Expertise: Utilize and manage Vector Databases to store and retrieve embeddings for efficient similarity search and context management for AI models. Experience with Graph Databases and RAG (Retrieval\-Augmented Generation) techniques is highly desirable.
- Serverless Architecture: Design and implement AI solutions using serverless technologies, primarily Cloudflare Workers and AWS Lambda. This will involve creating scalable, efficient, and cost\-effective AI\-powered services.
- Collaboration: Work closely with product managers, UX designers, and other engineers to translate business requirements into technical specifications and deliver high\-quality AI features.
- Performance Optimization: Continuously monitor, evaluate, and optimize the performance of AI models and integrations, ensuring accuracy, efficiency, and scalability.
- Stay Current: Keep abreast of the latest advancements in AI, machine learning, and natural language processing, and proactively identify opportunities to incorporate new technologies and techniques into our platform.
Key Requirements
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- Proven AI Experience: Demonstrated experience in developing and deploying AI solutions, with a strong focus on GPT integrations and Tool Calling functionalities.
- Database Proficiency: Strong experience working with Vector Databases. Experience with Graph Databases (and RAG implementation) is highly desirable. Experience with traditional relational SQL Server/Postgres SQL databases.
- Serverless Development: Experience with serverless technologies, specifically Cloudflare Workers.
- Programming Languages: Proficiency in Rest APIs, Typescript, JavaScript, CSS and HTML. Experience with C\# .NET Core is a strong plus.
- AI Regulations / Security: Understanding of AI risk assessment, legal regulations, configuring Guardrails and familiarity with common attack methods against AI systems (e.g. adversarial attacks, data poisoning)
- Data Analysis: Strong data analysis skills, with a proven ability to extract insights from large datasets. Experience in the retail sector and understanding of retail customer journeys is essential.
- Problem\-Solving: Excellent problem\-solving and analytical skills, with the ability to quickly understand and address complex technical challenges.
- Communication: Strong communication and collaboration skills, with the ability to effectively work in a team environment.
- Bachelor's Degree: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. (Master's degree or PhD preferred, but not required if significant practical experience is demonstrated).
Additional Desired Qualifications
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- Experience with SPARQL or Gremlin query languages for Graph Databases.
- Experience with any of the major cloud platforms (AWS, Azure, GCP).
- Contributions to open\-source AI projects.
- Experience working in an Agile development environment.
- Experience developing business\-to\-consumer (B2C) SaaS products, particularly in customer service.
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
How to Apply
- This role is currently advertised on LinkedIn and on our website at www.powerfront.com/jobs. If you're ready to make an impact and be part of a team that’s building the future of AI\-driven customer engagement, we’d love to hear from you
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
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Powerfront Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Powerfront Inc AI Hiring
Powerfront Inc has 6 open AI roles right now. They're hiring across Prompt Engineer, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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