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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.
We are looking for an experienced Senior Backend Developer with a specialization in integrations with third\-party SaaS solutions to join our team. In this role, you will play a pivotal role in architecting, implementing, and maintaining seamless integrations between our core systems and various external platforms such as CRMs, product databases, and knowledge bases. The ideal candidate will have expert knowledge in C\# .NET Core, SQL Server, and REST API development, with a strong emphasis on writing efficient, scalable, and secure code. Additionally, familiarity with serverless solutions like Firebase or Cloudflare Workers is highly desirable.
Key Responsibilities
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1\. Collaborate with cross\-functional teams including product managers, front\-end developers, and QA engineers to understand integration requirements and translate them into technical solutions.
2\. Design and architect robust backend systems to facilitate seamless integrations with third\-party SaaS solutions, ensuring scalability, reliability, and performance.
3\. Develop RESTful APIs using C\# .NET Core to expose and consume data from internal and external systems, adhering to best practices and design patterns.
4\. Interface with various external APIs, including CRMs, product databases, knowledge bases, and other SaaS platforms, to retrieve and synchronize data.
5\. Implement asynchronous processing and messaging patterns using async/await methods to handle long\-running operations and improve system responsiveness.
6\. Optimize database queries and data access patterns in SQL Server to improve performance and minimize latency in integration workflows.
7\. Ensure the security of backend systems by following OWASP security best practices, implementing proper authentication, authorization, and data encryption mechanisms.
8\. Stay updated on the latest developments in serverless computing and explore opportunities to leverage solutions like Firebase or Cloudflare Workers for specific integration scenarios.
9\. Collaborate with DevOps engineers to deploy and monitor backend services in cloud environments, ensuring high availability and scalability.
10\. Conduct code reviews, write technical documentation, and provide mentorship to junior developers to foster a culture of continuous learning and improvement.
Key Requirements
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1\. Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent work experience).
2\. Proven experience as a Backend Developer, with a focus on integration projects and working with third\-party APIs.
3\. Expertise in C\# .NET Core development, with a strong understanding of asynchronous programming, dependency injection, and multithreading.
4\. Proficiency in SQL Server, including database design, optimization, and writing efficient T\-SQL queries.
5\. Experience developing and interacting with RESTful APIs, including authentication mechanisms such as OAuth and JWT.
6\. Familiarity with integration patterns and protocols such as Webhooks, SOAP, and GraphQL.
7\. Knowledge of CRM platforms (e.g., Salesforce, HubSpot), product databases, and knowledge bases, and experience integrating with them.
8\. Experience with serverless solutions such as AWS Lambda, Firebase Functions or Cloudflare Workers is a plus.
9\. Strong focus on writing clean, maintainable, and efficient code, with a commitment to following best practices and coding standards.
10\. Excellent problem\-solving skills, with the ability to analyze complex integration requirements and design scalable solutions.
If you are passionate about backend development and have a strong background in building integrations with third\-party SaaS solutions, we encourage you to apply and join our innovative team.
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 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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