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About This Role
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
This role is a senior individual contributor responsible for shaping Presidio Cyber's point of view, service offerings, and client engagements at the intersection of artificial intelligence and cybersecurity. The role spans two converging missions: securing the AI systems our clients build and deploy, and operationalizing AI to advance security outcomes and reduce client risk. The Strategist works across Presidio's three security solution business units, Application \& Data Security, Identity \& Access Management, and Infrastructure \& Network Security — to integrate AI security capabilities into the broader portfolio, develop go\-to\-market plays with anchor OEM and emerging vendor partners, and represent Presidio externally as a credible voice on AI risk and AI\-augmented defense
Responsibilities Include:
Practice Strategy \& Offering Development
- Define and evolve Presidio's AI security strategy, point of view, and service portfolio across advisory, implementation, and managed services.
- Build the AI security offering catalog, pricing constructs, and delivery playbooks in partnership with practice leadership and the delivery org.
- Establish the roadmap for Secure\-AI\-by\-Design assessments, AI/ML threat modeling, AI\-SPM deployment, runtime defense, and agentic risk reviews.
Client Engagement \& Advisory
- Serve as senior advisor to CISOs, CIOs, and AI/ML leaders on AI security strategy, governance, and roadmap.
- Lead executive workshops, AI risk assessments, and threat modeling sessions at named strategic accounts.
- Support complex pre\-sales pursuits as the AI security SME; partner with account teams to size, scope, and win strategic opportunities.
Vendor \& Ecosystem Strategy
- Own AI security relationships across anchor partners (Palo Alto Networks, CrowdStrike, Cloudflare) and emerging vendors (Protect AI, HiddenLayer, Lakera, Prompt Security, Lasso, Mend AI, and adjacent).
- Evaluate new entrants, conduct technical due diligence, and recommend portfolio additions and rationalization.
- Partner with OEM alliance teams on joint solution architecture, co\-selling motions, and field enablement.
Cross\-SBU Integration
- Application \& Data Security: integrate AI\-SPM, model security testing, data lineage, and AI/ML threat modeling into AppSec and DataSec offerings.
- Identity \& Access Management: define the agent identity, non\-human identity (NHI), and agentic access control posture in partnership with Okta, CyberArk, and SailPoint.
- Infrastructure \& Network Security: align AI gateway, traffic inspection, and runtime defense capabilities with the Zero Trust, SSE, and SASE roadmap.
Thought Leadership \& Field Enablement
- Author points of view, white papers, and reference architectures; represent Presidio at RSAC, Black Hat, Gartner, and major OEM events.
- Build and deliver internal enablement for the field — practice architects, account executives, and delivery — on AI security narratives, demos, and qualification questions.
- Contribute to the Field CISO content cadence and client intelligence briefs.
Required Skills and Professional Experience:
- Bachelor's degree or the equivalent work experience and/or military experience
- 10\+ years in cybersecurity, including 3\+ years working directly on AI/ML security, GenAI deployment risk, or applied AI in security operations.
- Demonstrated track record advising large enterprise clients on AI risk strategy at the CISO and board level.
- Deep working knowledge of GenAI and agentic AI architectures: LLMs, RAG, vector stores, model serving, MCP, agent frameworks, and orchestration patterns.
- Fluency with the AI security threat landscape: prompt injection, training data poisoning, model extraction, supply chain compromise, agent exploitation, and data exfiltration via AI surfaces.
- Familiarity with relevant frameworks and regulations: OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Hands\-on or close working knowledge of AI security tooling: AI\-SPM (Wiz AI\-SPM, Prisma AIRS), runtime defense (Lakera, Lasso, Prompt Security, Protect AI), and red\-teaming tools (Garak, PyRIT).
- Strong executive presence and written communication; comfortable producing and delivering board\-level materials.
- Bachelor's degree in Computer Science, Engineering, or related field; advanced degree preferred.
- Must be a US Citizen
- Ability to obtain Government Security Clearance
Preferred Skills and Professional Experience:
- Prior pre\-sales, advisory, or practice leadership experience at a national solutions provider, GSI, Big 4, or boutique cyber firm.
- Existing relationships across the AI security vendor ecosystem.
- Published research, conference talks, or active community contributions on AI security topics.
- Industry certifications: CISSP, CCSP, or CISM; AI\-specific credentials a plus.
- Hands\-on experience deploying or securing at least one major AI platform (AWS Bedrock, Azure AI Foundry, GCP Vertex AI, OpenAI, Anthropic).
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
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*Applications will be accepted on a rolling basis.*
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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 3,708 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 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. C-Level-level AI roles across all categories have a median of $250,000.
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.
Presidio AI Hiring
Presidio has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Positions span Austin, TX, US, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 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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