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About This Role
About AppDirect
Become a digital, global citizen and enable the new generation of digital entrepreneurs around the world. AppDirect offers a subscription commerce platform to sell any product, through any channel, on any device - as a service. We power millions of subscriptions worldwide for organizations. We do this by our values-driven culture - one that enables you to Be Seen, Be Yourself, and Do Your Best Work.
About Devs.ai, an AppDirect Subsidiary
Devs.ai is an AI-native incubation inside AppDirect and the fastest-growing part of the business. We're a small, high-caliber team building products that change how AI and software gets created and deployed.
We operate like a startup with the backing of a scaled platform, No bureaucracy, no politics. Just smart people shipping fast and solving hard problems. Our flagship products are private AI workspaces that give access to AI chat, agent building, app building, CLI tools, enterprise search, and more.
We're early, we're growing, and we're looking for people who want to build something that matters.
About You
We are seeking a high-performing Category Sales Specialist – AI (formerly Product Sales Specialist) to accelerate the growth of our devs.ai offering across our channel ecosystem and direct sales motions.
As the domain expert for our AI category, you will bridge the gap between complex AI capabilities and enterprise business outcomes. You will act as the primary catalyst for driving devs.ai revenue by working closely with AppDirect Technology Advisors (channel partners), internal Account Executives, Sales Engineers, and customer stakeholders to uncover, co-sell, and close high-value AI opportunities.
Core Platform Pillars:
- Model-Agnostic Access (50+ LLMs): Users get centralized, instant access to over 50 leading Large Language Models (OpenAI, Anthropic, Gemini, Llama, etc.) under one unified subscription and workspace.
- No-Code App & Agent Builder: Drag-and-drop toolkits allow users to design and launch purpose-built AI agents in minutes, complete with custom personalities, workflows, and prompts.
- Enterprise Security & Privacy (Private Azure Instances): Proprietary enterprise data is never used to train public LLMs. Applications run in secure environments with built-in Role-Based Access Control (RBAC), Single Sign-On (SSO), and SOC 2 / ISO compliance.
- Unified Enterprise Knowledge Search (RAG): Built-in Retrieval-Augmented Generation allows AI agents to securely connect to private data stores, drive folders, internal files, and 2,000+ business software integrations (Salesforce, Microsoft 365, Google Workspace, etc.).
- AI Cost Transparency & Control: Provides real-time visibility into AI token consumption and spend across departments, preventing unexpected bills and giving IT full governance.
- Marketplace Distribution & Monetization: Built on AppDirect's commerce DNA, devs.ai allows enterprises, developers, and partners to distribute custom AI apps internally through a Private AI Store or monetize and sell them externally through AppDirect's channel network.
What you'll do and how you'll have an impact
- Drive AI Category Growth & Revenue: Exceed monthly, quarterly, and annual sales quotas for the devs.ai product portfolio.
- Channel Advisor Enablement & Co-Selling: Partner with AppDirect Channel Managers and Technology Advisors to identify, qualify, and close devs.ai opportunities across their end-customer accounts.
- Drive Ecosystem Awareness: Educate and train our advisor network on how to uncover enterprise AI pain points (e.g., shadow AI, lack of data governance, LLM cost management) and position devs.ai as the premier agentic AI platform.
- Consultative Selling & Demonstrations: Lead compelling product demonstrations showcasing how devs.ai builds no-code agents, connects to enterprise systems, and provides multi-LLM flexibility. Lead strategic discovery discussions with technical evaluators (CTOs, IT leaders, developers) and executive buyers (CEOs, CFOs).
- Solution Positioning: Articulate devs.ai's key differentiators—including model flexibility without vendor lock-in, private data guardrails, unified enterprise search, and AI spend management.
- Cross-Functional Collaboration: Partner with devs.ai Product Leadership, General Managers, Solution Engineers, and AppDirect Account Executives to refine sales messaging, communicate market feedback, and execute vertical-specific go-to-market strategies.
- Pipeline Management: Maintain an accurate pipeline forecast and execute targeted campaign follow-ups generated through marketing toolkits, advisor webinars, and developer events.
What we're looking for
- Sales Experience: 4–7+ years of quota-carrying B2B SaaS sales or specialty sales experience, ideally in emerging technology, enterprise software, developer platforms, or channel ecosystems.
- AI & Technical Literacy: Deep familiarity with the modern AI landscape (Generative AI, Large Language Models/LLMs, RAG, AI agents, enterprise knowledge search, and data privacy/compliance). Ability to translate technical capabilities into clear business value.
- Channel & Advisor Awareness: Proven track record working with or selling through indirect channels, technology brokerages/advisors, MSPs, or distribution platforms. Strong understanding of co-selling dynamics.
- Consultative Sales Mastery: Skilled in managing complex sales cycles involving multiple decision-makers across IT, Operations, Finance, Legal, and Security.
- Startup Mindset, Scale Execution: Comfortable operating with agility in a fast-paced, high-growth product unit while leveraging the massive reach and resources of an established platform company.
- Communication Skills: Exceptional presentation, storytelling, and negotiation skills.
At AppDirect, we believe that innovation thrives in an environment that houses diversity of excellence, experience and thought. We respect each AppDirector as their own fingerprint; unique with no one alike. We foster an environment of inclusion without regard to race, religion, age, sexual orientation, or gender identity enabling AppDirectors to embrace their uniqueness to do their best work. As such, we strongly encourage applications from Indigenous peoples, racialized people, people with disabilities, people from gender and sexually diverse communities, and/or people with intersectional identities.
At AppDirect we take privacy very seriously. For more information about our**use and handling of personal data from job applicants, please read our Candidate Privacy Policy. For more information of our general privacy practices, please see AppDirect Privacy Notice:
https://www.appdirect.com/about/privacy-notice
At AppDirect, AI tools may assist our recruitment team with administrative automations — always under human oversight. AI tools do not make hiring decisions or solely automated decisions about your candidacy – all decisions are made by our people. By submitting your application, you acknowledge that your information may be processed in this way. You may request access or deletion at any time by contacting [email protected].
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AppDirect, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
AppDirect AI Hiring
AppDirect has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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