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About This Role
Expedient is a full\-stack technology services provider that helps mid\-market and enterprise organizations modernize their infrastructure, manage their data, and deploy AI safely at scale. With 25 years in business, a 99 percent client retention rate, a 100 percent uptime SLA, and 200\+ active technology certifications, Expedient delivers managed cloud, data, and AI services under a single operating model: Intelligent Infrastructure.
AI CTRL is Expedient's enterprise AI platform. It is the governed environment through which every prompt, every response, and every automated AI workflow inside a customer's organization is routed, monitored, and controlled. The platform is organized around six pillars: Secure AI Gateway, Multi\-Model Chat, Compliance and Observability, AI Data Connectors, Agentic Workflow Engine, and Private Model Hosting.
About the Role:
The Manager, Product Operations supports the AI CTRL product organization with the reporting, tracking, and operational discipline that scaled product lines depend on. The role pairs with Principal Product Managers and product leadership to maintain a clear view of how the portfolio is performing and where it is headed.
You will own reporting infrastructure, OKR tracking cadence, order\-to\-cash inputs, and CPQ inputs for the AI CTRL portfolio. You will deliver a clean, current view of bookings, revenue, costs, partner performance, and customer outcomes that informs the work of product, sales, and finance teams across the organization.
This role reports to the Senior Director, AI CTRL Product. It is a sales operations function applied to product: builder mindset, automation instinct, and operational rigor.
What You Will Do:
Performance Reporting and Analytics
- Build and maintain automated reporting for bookings, revenue, gross margin, consumption, and unit economics across all AI CTRL product lines.
- Build Retool\-based dashboards that replace the manual spreadsheet work product, sales, and finance teams do today.
- Track and report partner\-sourced revenue, partner\-attached deal size, certified partner count, and time\-to\-first\-deployed\-agent against committed targets.
- Maintain trial\-to\-paid conversion, time\-to\-value, churn, and net retention reporting across pillars.
- Surface trends, anomalies, and exception patterns to product leadership early, with enough context to act on.
Order\-to\-Cash and CPQ Inputs
- Partner with IT and Sales Operations to maintain CPQ inputs for AI CTRL: product configurations, pricing rules, discount structures, bundle definitions, and approval workflows.
- Coordinate updates to CPQ when pricing, packaging, or product definitions change, with clear ownership of who signs off on what.
- Maintain order\-to\-cash hygiene across quote, provisioning, billing, and renewal stages, in partnership with IT.
- Track cost components (model costs, infrastructure costs, support costs, partner costs) by product line so the Principal Product Managers can make pricing and packaging decisions on real data.
OKR Tracking
- Run the weekly and monthly OKR tracking cadence for the AI CTRL product organization.
- Prepare OKR status materials, progress narratives, and exception lists for product leadership review.
- Maintain a defensible source\-of\-truth view of AI CTRL portfolio performance that the broader organization can reference.
Cross\-Functional Orchestration
- Serve as the AI CTRL product organization's primary working interface with IT, Finance, Sales Operations, and Customer Success Operations.
- Translate product needs into clear, actionable specifications for Salesforce, Sigma, billing, and reporting platforms.
- Coordinate with Sales Operations to align pipeline definitions, stage definitions, and reporting methodology across AI CTRL.
- Partner with Finance on cost allocation inputs and pricing change implementation.
Tooling and Automation
- Build automated reports, alerts, and operational tools using Retool and comparable platforms. Replace manual spreadsheet processes with engineered ones.
- Maintain the data dictionary for AI CTRL: metric definitions, pillar taxonomy, partner classification, and reporting cadence.
- Identify operational bottlenecks across the product lifecycle and either automate them or escalate them to IT with a clear specification.
- Establish data quality discipline so reports across the AI CTRL organization tie to the same numbers.
What We Are Looking For:
Required Qualifications
- 5\+ years of experience in product operations, business operations, revenue operations, sales operations, or strategic finance at a B2B SaaS or technology company.
- Track record of building reporting and automation from scratch, not just consuming reports built by others.
- Hands\-on fluency with Salesforce as a system of record, including report and dashboard construction, data hygiene, and CPQ configuration.
- Hands\-on fluency with Sigma, or a comparable analytics platform such as Looker, Tableau, ThoughtSpot, or Mode.
- Hands\-on fluency with Retool, or a comparable internal tooling and automation platform such as n8n.
- Strong SQL competence. You can write the query, not just read it.
- Working knowledge of SaaS commercial metrics: bookings, ARR, gross margin, NRR, and consumption economics.
- Experience working across product, sales, finance, and IT, with the ability to translate between technical and commercial audiences.
- Bias to action. You ship the report rather than schedule the meeting about the report.
- Bachelor's degree in Business, Finance, Economics, Computer Science, or a related field.
Preferred Qualifications
- Prior experience in managed services, cloud, CX, or SaaS environments serving mid\-market and enterprise customers.
- Direct experience with Salesforce CPQ or comparable quote\-to\-cash configuration platforms.
- Familiarity with consumption\-based and usage\-based pricing models, and the unit economics that govern them.
- Experience supporting a channel or partner program from the operations side.
- Familiarity with the AI infrastructure landscape and the cost drivers that come with it (token costs, inference costs, vector store costs).
Location and Travel:
This role is hybrid out of one of Expedient's offices in Pittsburgh, PA, Cleveland, OH, or Columbus, OH. Travel is minimal, typically less than 10 percent.
Salary for this position is directly related to your own experience, knowledge, and skills. Estimated range for this role is $115,000 to $125,000
\#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 $115K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($120K) sits 34% below the category median. Disclosed range: $115K to $125K.
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.
Expedient AI Hiring
Expedient has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in US. Compensation range: $125K - $180K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 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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