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About This Role
AI Solutions Engineer
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AI Systems, Automation \& Integrations
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About Ledge
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Ledge Inc. / Ledge Tech helps companies turn complex business problems into practical systems, tools, and workflows that create measurable value. We work at the intersection of business operations, technology, automation, and AI — helping teams move faster, reduce manual work, improve decision\-making, and bring useful tools to market.
We are building a team that is curious, technical, practical, and execution\-focused. Our work is not about AI hype. It is about building real systems that solve real problems.
About the Role
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We are looking for an AI Solutions Engineer to help design, build, test, and launch AI\-powered tools, automations, and connected systems for internal operations and customer\-facing projects.
This is a hands\-on builder role for someone who enjoys figuring things out. You should be comfortable taking a messy business problem, understanding the workflow, identifying the right data sources, connecting systems, building a working tool, validating the results, and improving it until it is useful, reliable, and repeatable.
The right person will combine technical ability with business judgment. You do not need to know every tool or platform already, but you should be eager to learn, able to move quickly, and motivated by building systems that people actually use. This is a combined work from home and business office as needs dictate.
What You’ll Work On
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You will help build AI\-enabled tools and workflows that may include:
- Internal tools that reduce repetitive work and improve team productivity
- Customer\-facing AI demos, pilots, and implementation projects
- Automations that connect data across business systems
- AI workflows that retrieve, analyze, summarize, structure, or validate information
- Integrations with APIs, databases, CRMs, ERPs, payment platforms, banking systems, spreadsheets, and document repositories
- Systems that improve speed, accuracy, repeatability, and cost efficiency
- Tools that move from prototype to production quickly
What You’ll Do
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- Build AI\-powered tools, workflows, agents, automations, and internal systems
- Connect systems and data sources using APIs, databases, webhooks, automation tools, and integrations
- Translate business problems into workflows, system requirements, prompts, logic, and validation steps
- Validate AI outputs against source data, business rules, expected results, and user requirements
- Optimize tools for accuracy, repeatability, speed, credit/API usage, and maintainability
- Support demos, pilots, launches, and customer\-facing implementations
- Document workflows, assumptions, test cases, integrations, and user instructions
- Work directly with leadership, project teams, customers, and technical partners
- Help shape how Ledge builds, tests, deploys, and improves AI\-enabled systems
What We’re Looking For
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We are looking for someone who is both technical and business\-minded — someone who can understand the problem, build the solution, and think through whether it actually creates value.
Strong candidates may have experience with:
- AI tools, agents, prompt design, workflow automation, or internal tool development
- APIs, databases, webhooks, data mapping, integrations, or system\-to\-system data exchange
- Business systems such as CRMs, ERPs, payment platforms, banking systems, accounting systems, or operational software
- Automation platforms such as Zapier, Make, n8n, Power Automate, Airtable, HubSpot, or similar tools
- Programming or scripting, especially Python, JavaScript, SQL, or API\-based development
- Business operations, project execution, customer implementation, process improvement, or technology consulting
- Building tools that are accurate, repeatable, fast, cost\-conscious, and easy for others to use
Nice to Have
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- Experience with OpenAI, ChatGPT, Claude, Azure AI, Microsoft Copilot, or similar AI platforms
- Experience with Stripe, Plaid, QuickBooks, NetSuite, SAP, Epicor, Microsoft Dynamics, HubSpot, Salesforce, or similar platforms
- Experience with payment system integrations, ERP integrations, banking data integrations, financial workflows, reconciliation, or accounting systems
- Experience using structured outputs, retrieval workflows, vector databases, AI agents, or AI APIs
- Experience working directly with customers on technical projects, implementations, or demos
- Experience in a startup, consulting, product development, operations, or systems\-building environment
What Success Looks Like
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- You build tools that people actually use
- You move quickly from idea to prototype to working system
- You connect the right data sources and make workflows repeatable
- You improve speed, accuracy, and efficiency across internal and customer workflows
- You reduce manual work through practical automation
- You balance technical execution with business impact
- You document systems clearly enough that others can use, test, and improve them
- You help Ledge and its customers turn AI ideas into real operational value
Who Will Thrive in This Role
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You will do well here if you are:
- Curious and self\-directed
- Practical and execution\-focused
- Comfortable with ambiguity
- Strong at learning new tools quickly
- Detail\-oriented and validation\-minded
- Interested in both technology and business
- Able to communicate clearly with technical and non\-technical people
- Motivated by building useful systems, not just impressive demos
- Excited by the opportunity to help shape a growing AI and technology practice
Compensation
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This role will include a competitive base salary with an incentive structure tied to performance, project execution, and business impact. Details will be discussed during the interview process.
Why This Role Matters
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AI is changing how companies operate, but most organizations need help turning potential into practical systems. At Ledge, this role will help build those systems.
You will have the opportunity to work close to the front edge of applied AI — building tools, connecting systems, solving real business problems, and helping shape how companies adopt AI in a practical, measurable way.
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Ledge 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Ledge Inc. AI Hiring
Ledge Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Spring Grove, PA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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