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About This Role
Expedient helps mid\-market and enterprise organizations modernize infrastructure, manage data, and deploy AI safely at scale. We have been in business 25 years, retain 99 percent of our clients, operate under a 100 percent uptime SLA, hold 200\+ active technology certifications, and employ technical staff in 70 percent of roles. We deliver managed cloud, data, and AI under a single operating model: Intelligent Infrastructure.
AI CTRL is Expedient's enterprise AI platform. Prompts, responses, and automated workflows inside a customer's organization are routed, governed, and measured through it.
About the Role:
The Agentic Workflow Engine is where AI CTRL customers move from chat to automation. The work is turning our Agentic Workflow platform, combined with Expedient delivery, into a productized and repeatable offering with clear positioning, packaging, and enablement.
This is a product marketing role. You will own how AWE is positioned, packaged, priced, launched, and sold, and you will manage the relationship with a small set of professional services partners who deliver AWE work. You will report to the Head of Product and work alongside the AI CTRL product managers, the solutions architect team, and delivery.
What You'll Be Doing:
Positioning and messaging* Develop and maintain the positioning and messaging framework for AWE, including what it is, who it is for, and why customers choose Expedient rather than licensing Retool directly.
- Maintain competitive positioning against workflow/agent builder solutions, and in\-house builds.
Packaging and pricing* Recommend packaging and price points for the offering, and define what is included at each level.
- Define how AWE attaches to the Secure Gateway and Chat installed base.
Customer evidence* Quantify the return on a deployed agent and maintain an ROI model the field can use with finance buyers.
- Maintain a quarterly cadence of customer case studies.
- Maintain the agent template catalog as a customer\-facing asset, organized by workflow, outcome, and industry.
Field and partner enablement* Define qualification criteria for AWE opportunities.
- Build and maintain the field kit: battle cards, discovery questions, demo platform and scripts, objection handling, one\-pagers, and the AWE section of the Product Guide working with marketing and solution architect teams.
- Manage the relationship with a select group of professional services partners who deliver AWE work on the Expedient stack. Serve as their point of contact, keep them current on the product, and make sure what they deliver reflects it.
- Provide those partners with the enablement they need to scope, price, and position AWE work independently.
- Define launch plans for each major release, including internal readiness, customer communications, and field activation.
- Run enablement sessions for sellers, solutions architects, and partners.
Demand generation* Partner with marketing on campaign briefs, web copy, and event positioning.
- Bring field and delivery feedback into the AWE roadmap on a regular cadence.
What We're Looking For:
Required* 5\+ years in B2B product marketing, or a combined product management and product marketing role, in SaaS or platform software.
- Experience launching and growing a product line, with a measurable effect on pipeline and win rate.
- Experience owning positioning, packaging, and pricing recommendations.
- Working fluency with AI products and concepts, including the difference between RAG and agentic workflows, and the ability to explain agent behavior accurately to a buyer.
- Hands\-on experience with a low\-code or workflow platform such as Retool, n8n, Dify, or Power Automate, including the ability to build a demo independently.
- Experience supporting B2B distribution through direct sales, channel partners, or both.
- Strong writing across formats, including one\-pagers, battle cards, and pricing memos.
Preferred* Experience marketing a product built on a resold or OEM platform.
- Experience working directly with systems integrators, consultancies, or delivery partners.
- Background in managed services, cloud, or infrastructure SaaS.
- Experience in workflow automation, iPaaS, RPA, or low\-code categories.
- Experience selling to or supporting mid\-market enterprises in the $50M to $2B revenue range.
This is a hybrid position requiring proximity to our office location in Pittsburgh, PA. Sponsorship is not provided for this role.
Salary for this position is directly related to your own experience, knowledge, and skills. Estimated range for this role is $150,000 to $180,000
\#LI\-Hybrid
WORKING FOR EXPEDIENT
We prioritize ongoing education and continuous innovation to remain at the forefront of the information technology landscape. Our commitment to learning is reflected in our comprehensive employee training and tuition reimbursement programs, which are driven by our employees and funded by Expedient 100%.
For our full\-time employees we offer an exceptional benefits package including three weeks of paid time off annually that increases with tenure plus your birthday off and a health holiday to be used for preventive care. We offer parental leave, top\-tier medical, dental, and vision, disability and life insurance, at an affordable rate, wellness engagement opportunities, and a 401(k) with a generous match.
We also recognize the importance of a comfortable and convenient work environment. We offer a hybrid work model for many roles, paid parking and other perks.
Expedient is an equal opportunity employer. Qualified applicants will receive fair and equitable consideration for employment without regard to their race, color, religion, national origin, gender, protected veteran status, disability, or any other characteristic protected by law.
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Salary Context
This $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Expedient, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $150K to $180K.
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.
Expedient AI Hiring
Expedient has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Pittsburgh, PA, US, US. Compensation range: $150K - $250K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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).
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 3,708 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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