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About This Role
Title: Manager- On-Premises, Cloud & AI Agent FinOps
Location: Newton Square, PA (hybrid, 3 days onsite every week)
Duration: Contract to hire after 3-6 months
Position Summary:
The Manager, On-Premises, Cloud & AI Agent FinOps is responsible for establishing and leading financial governance, cost optimization, and operational management of hybrid IT infrastructure environments, including on-premises data centers, public cloud platforms, and AI-powered agent ecosystems.
This leadership role partners with Infrastructure, Cloud Engineering, Platform Engineering, Enterprise Architecture, Finance, Security, Compliance, Procurement, and Business stakeholders to ensure technology investments deliver measurable business value while maintaining financial accountability, governance, and regulatory compliance.
The manager will oversee enterprise FinOps practices, AI Agent Governance, technology budgeting, forecasting, show back/chargeback reporting, cost transparency initiatives, and financial optimization across cloud, on-premises, and AI-driven services.
Primary Responsibilities:
FinOps Strategy & Financial Management:
- Develop and mature enterprise FinOps capabilities across cloud and on-premises infrastructure.
- Create financial visibility into compute, storage, networking, backup, virtualization, licensing, and platform services.
- Lead technology budget planning, forecasting, and variance analysis.
- Establish showback and chargeback models for business units and shared services.
- Manage cloud consumption costs and identify rightsizing opportunities.
- Create executive dashboards and financial reports for leadership.
- Partner with Finance and Procurement on contract reviews, renewals, and vendor negotiations.
- Drive continuous cost optimization initiatives across infrastructure platforms.
On-Premise Infrastructure Financial Governance:
- Maintain cost transparency for data center resources including servers, storage, backup, virtualization, and network services.
- Develop unit-cost metrics and capacity-based financial models.
- Analyze utilization trends and identify opportunities to reduce stranded capacity.
- Support infrastructure lifecycle planning and capital investment decisions.
- Evaluate infrastructure modernization opportunities to reduce operational costs.
Cloud Financial Operations:
- Manage financial governance across Azure, AWS, and SaaS services.
- Implement tagging, allocation, and cost attribution standards.
- Monitor cloud spend and consumption trends.
- Develop forecasting models and budget projections.
- Lead cloud cost optimization programs including: + Rightsizing + Reservation strategies + Savings plans + Storage optimization + Workload placement decisions
AI Agent Governance & Financial Oversight:
- Establish and manage the enterprise AI Agent Governance framework.
- Maintain inventories of approved AI agents, copilots, and intelligent automation solutions.
- Develop governance processes for AI lifecycle management.
- Partner with Security, Legal, Compliance, and Data Governance teams to ensure Responsible AI adoption.
- Monitor AI usage, token consumption, model costs, and platform utilization.
- Implement financial accountability and chargeback models for AI platforms and services.
- Evaluate ROI and business value of AI investments.
- Define governance controls for AI risk, privacy, security, and regulatory compliance.
Reporting & Analytics:
- Develop executive-level dashboards and KPIs.
- Measure cloud, infrastructure, and AI financial performance.
- Track savings opportunities and cost avoidance initiatives.
- Provide financial recommendations to senior leadership.
- Utilize Power BI, analytics platforms, and FinOps tooling to create actionable insights.
Vendor & Tool Management:
- Evaluate and manage FinOps, ITFM, and AI governance platforms.
- Lead vendor assessments, RFPs, and technology evaluations.
- Manage strategic partner relationships.
- Ensure technology investments align with business objectives and financial goals.
Leadership & Team Management:
- Foster a culture of accountability, innovation, and continuous improvement.
- Collaborate across Infrastructure, Security, Cloud Operations, Platform Engineering, and Finance teams.
- Present financial and governance recommendations to executive leadership.
Required Qualifications:
Education:
- Bachelor's degree in Information Technology Computer Science, Finance, Accounting, Business Administration, or related field.
Experience:
- 8-12+ years of experience in IT Infrastructure, Cloud Operations, Financial Management, FinOps, or Platform Engineering.
- 3-5+ years of leadership or management experience.
- Experience managing hybrid cloud and on-premises environments.
- Experience with budgeting, forecasting, and financial planning.
- Experience implementing FinOps practices and governance frameworks.
- Experience with AI, automation, Copilot, or AI Agent platforms preferred.
Technical Skills:
Cloud Platforms:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (Preferred)
Infrastructure Technologies:
- VMware vSphere / ESXi
- Storage Platforms
- Backup & Recovery Solutions
- Networking & Data Center Technologies
FinOps & Financial Management:
- Budget Planning
- Forecasting
- Chargeback / Showback
- Cost Allocation
- Cost Optimization
- Technology Financial Management (TBM/ITFM)
AI & Governance:
- Microsoft Copilot
- Azure AI Services
- AI Agent Governance Frameworks
- Responsible AI Controls
- Data Governance
- AI Risk Management
Analytics & Reporting:
- Power BI
- Excel
- Financial Modeling
- Dashboard Development
- Executive Reporting
Preferred Certifications:
Cloud:
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Administrator Associate
- AWS Certified Solutions Architect
FinOps:
- FinOps Certified Practitioner
- FinOps Certified Professional
AI:
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Applied Skills for Copilot and AI Solutions
Success Measures:
- Improved cloud and infrastructure cost transparency
- Annual cost savings and optimization targets achieved
- Accurate budget forecasting and variance management
- Successful implementation of AI governance controls
- Increased accountability through showback/chargeback models
- Executive adoption of financial and operational reporting
- Demonstrated ROI from AI and cloud investments
Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At VeeRteq Solutions Inc., this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $215,000 based on 203 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
VeeRteq Solutions Inc. AI Hiring
VeeRteq Solutions Inc. has 3 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Agent Developer. Positions span Irving, TX, US, Chicago, IL, US, Newtown Sq, PA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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