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About This Role
Senior Project Manager – AI \& Enterprise ERP Solutions
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Company: Astute Business Solutions
Location: Remote (U.S. Based) \| Headquarters: Dublin, California
Employment Type: Full\-Time
Start Date: August 3, 2026
Salary: $130,000–$155,000 \+ Performance Bonus \+ Benefits
Travel: 15–25% (Client Sites and Industry Conferences)
About Astute Business Solutions
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Astute Business Solutions is a nationally recognized Oracle Partner with more than 20 years of experience delivering enterprise ERP consulting, managed services, cloud migrations, and AI\-powered business solutions. Founded and led by former PeopleSoft professionals, Astute has successfully completed hundreds of implementations, upgrades, cloud migrations, and managed services engagements for higher education, government, healthcare, financial services, and commercial organizations throughout North America.
Our core expertise includes:
- Oracle Cloud Infrastructure (OCI)
- Oracle PeopleSoft
- Oracle E\-Business Suite
- JD Edwards
- Ellucian Banner
- Oracle Database \& Middleware
- Managed Services
- Application Modernization
- Enterprise AI \& Intelligent Automation
Astute is investing aggressively in Agentic AI and Generative AI solutions that transform enterprise business processes. Our growing portfolio includes AI\-powered solutions for:
- Accounts Payable Automation
- Travel \& Expense Automation
- Contract Lifecycle Management (CLM)
- Procurement
- Intelligent Document Processing
- Financial Reporting
- Enterprise Search and Knowledge Assistants
- Campus Solutions
- Predictive Analytics
- AI Agents built on Oracle Cloud Infrastructure
As we continue expanding our AI practice, we are looking for an experienced Senior Project Manager to lead enterprise AI initiatives from concept through production deployment.
Position Overview
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This is a strategic leadership role responsible for delivering enterprise ERP and AI transformation projects for our customers.
The successful candidate will lead multidisciplinary teams consisting of ERP functional consultants, AI engineers, cloud architects, developers, data scientists, business analysts, and client stakeholders to successfully deliver innovative enterprise solutions.
The ideal candidate understands both traditional ERP implementations and modern AI\-enabled digital transformation initiatives.
Key Responsibilities
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### Enterprise Project Leadership
- Lead multiple enterprise implementations across PeopleSoft, Oracle E\-Business Suite, Banner, JD Edwards, and Oracle Cloud.
- Manage project scope, budgets, schedules, resource planning, risks, quality, and customer expectations.
- Establish governance, project standards, KPIs, and executive reporting.
- Drive successful project execution using Agile, Scrum, Waterfall, or Hybrid methodologies.
### AI Program Leadership
Lead delivery of Astute's next\-generation AI solutions, including:
- AI\-powered Accounts Payable Automation
- GenAI Travel \& Expense Automation
- Contract Lifecycle Management (CLM)
- Intelligent Document Processing
- AI Search \& Knowledge Assistants
- Predictive Analytics
- AI Agents for ERP business processes
Coordinate AI initiatives from:
- Business requirements
- Solution architecture
- Data preparation
- Model validation
- User acceptance testing
- Production deployment
- Continuous improvement
### Client Relationship Management
- Serve as the executive point of contact for customers.
- Build trusted relationships with CIOs, CFOs, Controllers, PMOs, and business leaders.
- Facilitate executive steering committee meetings.
- Communicate project status, risks, mitigation strategies, and business value.
### Cross\-Functional Leadership
Lead geographically distributed teams consisting of:
- ERP Functional Consultants
- Technical Developers
- Oracle Cloud Architects
- AI Engineers
- Data Scientists
- QA Engineers
- Infrastructure Specialists
- Client Project Teams
### Risk \& Change Management
- Identify project risks and mitigation strategies.
- Manage organizational change associated with AI adoption.
- Ensure projects comply with security, governance, and compliance requirements.
- Drive user adoption through effective communication and training strategies.
Required Qualifications
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- Bachelor's degree in Computer Science, Engineering, Business, Information Systems, or a related discipline.
- 10\+ years of enterprise project management experience.
- Minimum 5 years managing enterprise ERP implementations.
- Demonstrated experience delivering AI, automation, analytics, or intelligent document processing projects.
- Strong understanding of enterprise business processes across Finance, Procurement, Human Resources, and Supply Chain.
- Experience managing distributed consulting teams.
- Strong executive communication and presentation skills.
- Experience managing multiple concurrent customer engagements.
Preferred Experience
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Experience with one or more of the following:
- Oracle Cloud Infrastructure (OCI)
- Oracle PeopleSoft
- Oracle E\-Business Suite
- JD Edwards
- Ellucian Banner
- Oracle Integration Cloud (OIC)
- Intelligent Document Processing (IDP)
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- AI Agents
- Document AI
- OCR Technologies
- Microsoft Azure AI
- AWS AI Services
- Google Vertex AI
Certifications
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Required:
- PMP (Project Management Professional)
Preferred:
- PMI\-ACP
- Oracle Cloud Infrastructure Certification
- Scrum Master Certification
- AI or Machine Learning Certifications
- Prosci Change Management Certification
Why Join Astute Business Solutions?
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This is an opportunity to help define the future of AI within the Oracle ERP ecosystem.
At Astute, you will:
- Lead innovative AI transformation projects for enterprise customers.
- Work alongside experienced Oracle consultants and AI engineers.
- Influence the design and delivery of next\-generation enterprise AI products.
- Collaborate with executive leadership on strategic initiatives.
- Enjoy a flexible, fully remote work environment.
- Receive competitive compensation, performance bonuses, professional development opportunities, and long\-term career growth.
How to Apply
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Interested candidates should email their resume to [email protected] with the subject line:
Senior Project Manager – AI \& Enterprise ERP Solutions
Please include a brief cover letter highlighting your experience leading enterprise ERP, cloud, automation, or AI implementation projects.
Astute Business Solutions is an Equal Opportunity Employer. We are committed to building a diverse and inclusive workplace. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by applicable law.
Salary Context
This $130K-$155K 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 Astute Business Solutions, 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 ($142K) sits 35% below the category median. Disclosed range: $130K to $155K.
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.
Astute Business Solutions AI Hiring
Astute Business Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $155K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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