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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 Project Manager, AI Outcomes runs Expedient's complex customer engagements on the AI CTRL platform. These are multi\-month, SOW\-based engagements delivered by Expedient teams in partnership with the customer, with external delivery partners brought in when required, that map a customer's business process and ship the agentic workflow that automates it.
This role is the discipline that keeps every engagement on plan. You execute against the SOWs that govern each engagement: scheduling, staffing coordination, cost and milestone tracking, risk management, and the acceptance process. You will be the client's primary day\-to\-day Expedient counterpart for engagement execution.
This is a consulting\-flavored role inside a product company. You will bring the engagement management discipline of a consultancy, an agency, or a professional services firm into Expedient's AI CTRL delivery model.
What You Will Do:
Engagement Delivery
- Run multiple concurrent AI CTRL customer engagements end to end: kickoff, discovery, design, build, test, acceptance, and handoff to Customer Success.
- Maintain integrated project plans across internal delivery teams, customer teams, and partner teams.
- Drive the engagement cadence: standups, weekly status, steering committee, change requests, and executive escalations.
- Hold the line on critical path. Identify blockers early, escalate cleanly, and unblock without ceremony.
Plan, Cost, and Schedule
- Execute against the SOW from kickoff through final invoice: milestones, deliverables, acceptance criteria, and payment triggers.
- Track burn rate, forecast to completion, and engagement spend against plan. Surface variances to the Solutions Architect and engagement leadership before they become material.
- Identify out\-of\-scope work as it emerges and escalate to the Solutions Architect for change order action. No quiet absorption of scope drift.
- Protect schedule and quality by managing staffing handoffs, dependency risk, and rework drivers.
Business Process Engineering
- Lead business process engineering work with customers. Map existing processes, identify automation candidates, design future\-state workflows, and translate them into agent specifications the engineering team can build to.
- Run workshops with customer subject matter experts and executive sponsors to validate process redesign and secure adoption commitment.
- Capture and codify process patterns across engagements so the next customer engagement starts faster.
Client Partnership and Stakeholder Management
- Serve as the primary day\-to\-day Expedient counterpart for engagement execution. Build working trust with engagement sponsors, business stakeholders, and IT counterparts.
- Prepare and run steering committees in partnership with the Solutions Architect. Lead the operational portions of executive readouts that connect engagement progress to business outcomes.
- Manage expectations on schedule, cost, and outcomes with honesty and a clean paper trail.
Cross\-Functional Orchestration
- Coordinate internal delivery teams, Solutions Architects, and Customer Success.
- Manage the working relationship with delivery partners and subcontractors when an engagement requires external capacity. Track adherence to partner SOWs, which the partners own.
- Serve as the operating bridge between the customer engagement and the AI CTRL product organization. Feed customer signal back to product on a known cadence.
Risk, Quality, and Acceptance
- Maintain a live risk and issue log on every engagement. Escalate with proposed mitigations, not just problems.
- Enforce the acceptance criteria defined in the SOW. Run the milestone and final acceptance process with the customer.
- Conduct lessons\-learned and engagement post\-mortems with discipline. Codify what worked and what did not.
What We Are Looking For:
Required Qualifications
- 8\+ years of project management experience, including at least 4 years in a consulting firm, agency, systems integrator, or professional services environment.
- Demonstrated experience executing SOW\-based customer engagements end to end, with measurable scope, milestones, cost tracking, and acceptance.
- Hands\-on experience with engagement financial tracking: budget management, burn rate, change order processes, and forecast\-to\-completion.
- Business process engineering background: mapping current state, designing future state, and translating process redesign into requirements engineering can build to.
- Experience coordinating multi\-party delivery: internal engineering teams, customer teams, and external partners or subcontractors.
- Excellent client\-facing communication. You can run a steering committee, write a one\-page status update to a CIO, and de\-escalate a difficult stakeholder conversation in the same day.
- Hands\-on fluency with project management tooling (Jira, Smartsheet, Microsoft Project, ClickUp, or equivalents) and customer collaboration tools (Slack, Teams, Notion, Confluence).
- Bias to action. You move issues to resolution faster than they age in a tracker.
Preferred Qualifications
- PMP, PgMP, or equivalent project management certification.
- Direct experience delivering AI, data, automation, or software implementation engagements for enterprise customers.
- Familiarity with agentic AI workflows and the tooling landscape (Retool, n8n, Dify, LangGraph, or comparable).
- Experience with earned value management or a comparable cost discipline framework.
- Prior experience in a managed services, cloud, CX, or SaaS environment serving mid\-market and enterprise customers.
- Bachelor's degree in Business, Engineering, Operations Research, or a related field. MBA welcome but not required.
How You Will Be Measured
- On\-time, on\-scope, on\-budget engagement delivery.
- Engagement budget adherence against plan.
- Customer satisfaction and reference\-ability at engagement close.
- Change order discipline: out\-of\-scope work identified and escalated, not absorbed.
- Acceptance and sign\-off cycle time.
- Reusable patterns contributed back to the AI Outcomes practice and the AI CTRL product organization.
Location and Travel:
This role is hybrid out of one of Expedient's offices in Pittsburgh, PA, Cleveland, OH, or Columbus, OH. Expect 25 to 40 percent travel for customer kickoffs, steering committees, workshops, and acceptance events.
Salary for this position will be based on your experience, knowledge and skills. Estimated salary range is $100,000 \- $125,000 annually.
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 $100K-$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 ($112K) sits 38% below the category median. Disclosed range: $100K 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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