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Presidio, Where Teamwork and Innovation Shape the Future
At Presidio, we're at the forefront of a global technology revolution, transforming industries through cutting\-edge digital solutions and next\-generation AI. We empower businesses \- and their internal customers \- to achieve more through innovation, automation, and intelligent insights.
The Role
The Solution Owner 1 for Solutions Engineering Group (SEG) is responsible for owning the strategy, roadmap, and lifecycle of an assigned AI solution within Presidio's AI Solutions portfolio. This individual contributor role serves as the single point of accountability for solution health, evolution, and value delivery — partnering with Engineering, Product, Sales, and Marketing to drive solution success from concept through retirement.
The Solution Owner works closely with Field Sales, Engineering leads, Product Management, Marketing, and customers to ensure the assigned solution meets market needs, delivers measurable customer outcomes, and evolves in alignment with Presidio's broader AI strategy. This role translates market insight and customer feedback into solution decisions that drive adoption, differentiation, and business value.
Responsibilities Include:
- Own the strategy, roadmap, and lifecycle of the assigned AI solution across Presidio's AI Solutions portfolio.
- Define and maintain solution vision, positioning, and value proposition in partnership with Marketing and Product.
- Gather and prioritize market, customer, and competitive insights to inform solution evolution.
- Partner with Engineering to define solution requirements, scope, and delivery milestones.
- Collaborate with Field Sales to support go\-to\-market activities, enablement, and key customer engagements.
- Track solution health metrics including adoption, customer satisfaction, financial performance, and technical health.
- Make solution\-level trade\-off decisions balancing customer value, technical feasibility, and business impact.
- Develop and maintain solution documentation including playbooks, FAQs, and seller enablement materials.
- Represent the solution in governance forums, steering committees, and customer executive reviews.
- Drive solution launches, updates, and sunset activities in coordination with cross\-functional teams.
Technology/Area of Specialization:
- Familiarity with AI/ML solution delivery lifecycles including design, development, deployment, and operations.
- Familiarity with product management and solution ownership best practices including roadmap and lifecycle management.
- Familiarity with cloud platforms, AI/ML services, and modern application architectures.
Basic Knowledge, Skills, and Abilities:
- Strong strategic thinking with the ability to translate market insight into solution decisions.
- Excellent communication and storytelling skills; ability to articulate solution value to diverse audiences.
- Cross\-functional collaboration skills; ability to influence across Engineering, Product, Sales, and Marketing.
- Financial fluency — can interpret solution financials, pricing, and ROI logic.
- Customer\-centric mindset with deep empathy for customer outcomes and adoption journeys.
- Comfort with ambiguity and ability to make decisions in a fast\-paced, evolving environment.
Required Skills and Professional Experience:
- 7\+ years of experience in product management, solution ownership, or related roles, preferably in AI/ML or technology solutions.
- Demonstrated experience owning solutions or products through full lifecycle stages.
- Familiarity with cloud platforms, AI/ML services, and modern solution architectures.
- Bachelor's degree in Business, Computer Science, Engineering, or related field — or equivalent experience.
Competencies Required:
- Strategic Thinking
- Customer Focus
- Decision Making
- Communication Excellence
- Leadership and Influence
- Achievement and Effort
Physical Requirements:
This role primarily involves working in an office or hybrid environment with extended periods of desk\-based computer work. Travel is approximately 10%, primarily to customer sites, Presidio offices, and industry events. Regular participation in virtual meetings and occasional on\-site customer and team sessions is expected.
Your future at Presidio
JoiningPresidio means stepping into a culture of trailblazers \- thinkers, builders, and collaborators \- who push the boundaries of what's possible. With our expertise AI\-driven analytics, cloud solutions, cybersecurity, and next\-gen infrastructure, we enable businesses to stay ahead in an ever\-evolving digital world.
Here, your impact is real. Whether you're harnessing the power of Generative AI, architecting resilient digital ecosystems, or driving data\-driven transformation, you'll be part of a team that is shaping the future.
Ready to innovate? Let's redefine what's next\-together.
About Presidio
Presidio is committed to hiring the most qualified candidates to join our amazing culture. We aim to attract and hire top talent from all backgrounds, including underrepresented and marginalized communities. We encourage women, people of color, people with disabilities, and veterans to apply for open roles at Presidio. Diversity of skills and thought is a key component to our business success.
At Presidio, speed and quality meet technology and innovation. Presidio is a trusted ally for organizations across industries with a decades\-long history of building traditional IT foundations and deep expertise in AI and automation, security, networking, digital transformation, and cloud computing. Presidio fills gaps, removes hurdles, optimizes costs, and reduces risk. Presidio's expert technical team develops custom applications, provides managed services, and enables actionable data insights and builds forward\-thinking solutions that drive strategic outcomes for clients globally. For more information visit
*Applications will be accepted on a rolling basis.*
*Presidio has a strong commitment to the community we serve and our employees. As an Equal Opportunity Employer, we strive to have a workforce that includes the community we serve.*
*Presidio is an Equal Opportunity Employer Disability/Vets. We evaluate qualified applicants without regard to race, color, religion, sex, age, national origin, disability, veteran status, genetic information, and other legally protected categories.*
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*Presidio EEO Policy Statement is available here: https://www.presidio.com/careers*
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Presidio, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Presidio AI Hiring
Presidio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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