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About This Role
As part of the Office of Technology \& Innovation , you will serve as a trusted advisor to Oracle’s most strategic clients. You will help executives understand the transformative potential of AI, analytics, and emerging technologies, while representing Oracle as a credible and forward\-looking voice in the market.
You will collaborate closely with sales, product management, engineering, and other internal teams to ensure Oracle’s strategy and solutions remain aligned with evolving client priorities and market opportunities.
Key ResponsibilitiesExecutive Engagement and Strategic Advisory* Serve as a trusted advisor to C\-level executives, including CIOs, CTOs, CEOs, CDOs, CHROs, and CFOs.
- Build and maintain long\-term relationships with senior client stakeholders.
- Develop a deep understanding of clients’ business priorities, transformation objectives, operating models, and technology environments.
- Lead executive briefings, workshops, strategy sessions, and presentations focused on AI, analytics, and Oracle Fusion Applications.
- Translate complex technical concepts into clear, compelling business language for executive and non\-technical audiences.
- Help clients develop practical strategies and roadmaps for adopting AI and analytics within their enterprise application landscape.
Oracle Fusion Applications Strategy* Articulate how AI, analytics, and emerging technologies can create value across Oracle Fusion Cloud Applications, including ERP, HCM, SCM, and CX.
- Advise clients on the role of enterprise applications, operational data, and embedded intelligence in business transformation.
- Connect Oracle’s application, data, analytics, and cloud capabilities into cohesive, outcome\-oriented client propositions.
- Demonstrate a strong understanding of Oracle Fusion Applications architecture, business processes, data models, and integration considerations.
- Position Oracle Fusion Data Intelligence and related analytics capabilities as strategic enablers of insight and decision\-making.
Thought Leadership and Industry Influence* Develop and communicate a compelling point of view on the future of AI, analytics, enterprise applications, and emerging technologies.
- Represent Oracle at executive forums, industry conferences, customer events, and other external engagements.
- Contribute to white papers, articles, presentations, blogs, and other thought leadership content.
- Monitor developments in AI, analytics, cloud technology, enterprise applications, and the competitive landscape.
- Help shape market conversations around responsible AI, data\-driven transformation, and the future of enterprise technology.
Strategic Sales Support* Partner with sales leaders and account teams to develop engagement strategies for key clients and strategic opportunities.
- Provide technical depth, executive credibility, and thought leadership throughout complex sales cycles.
- Help qualify opportunities, identify client needs, and develop differentiated solution strategies.
- Support the creation of executive presentations, proposals, demonstrations, and value propositions.
- Facilitate alignment among client executives, Oracle sales teams, solution specialists, product leaders, and technical stakeholders.
- Influence business outcomes by connecting Oracle’s capabilities to measurable client priorities and strategic objectives.
Product Strategy and Innovation* Provide client insights, market intelligence, and competitive feedback to Oracle product management and engineering teams.
- Identify emerging use cases, unmet client needs, and opportunities for new capabilities or offerings.
- Collaborate with product leaders to help ensure Oracle’s AI, analytics, and Fusion Applications roadmaps reflect evolving market requirements.
- Act as a bridge between executive clients and Oracle’s product and engineering organizations.
- Support innovation initiatives, prototypes, and strategic client programs involving AI and emerging technologies.
Internal Enablement* Educate sales, marketing, consulting, and technical teams on AI, analytics, emerging technologies, and Oracle Fusion Applications.
- Develop and deliver enablement materials, executive narratives, training sessions, and reusable client assets.
- Mentor colleagues and help strengthen Oracle’s executive engagement and technology storytelling capabilities.
- Foster collaboration, innovation, and knowledge sharing across the organization.
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Business, or a related discipline, or equivalent professional experience.
- At least 10 years of experience in AI, analytics, enterprise applications, cloud technology, consulting, or a related field, with progressively increasing responsibility.
- Demonstrable knowledge of Oracle Fusion Cloud Applications and the role of AI and analytics within enterprise business processes.
- Strong understanding of AI and analytics technologies, including machine learning, generative AI, natural language processing, predictive analytics, business intelligence, data warehousing, and modern data platforms.
- Proven experience engaging and influencing C\-level executives and other senior business and technology leaders.
- Experience leading executive presentations, strategic workshops, and complex client discussions.
- Ability to translate technical capabilities into business value, measurable outcomes, and transformation strategies.
- Experience in strategic consulting, pre\-sales, solution advisory, enterprise architecture, product strategy, or a comparable client\-facing role.
- Demonstrated ability to influence complex sales opportunities and build consensus across business, technical, and executive stakeholders.
- Exceptional written, verbal, presentation, and interpersonal communication skills.
- Strong analytical, problem\-solving, and strategic\-thinking capabilities.
- Ability to operate effectively across sales, product, engineering, consulting, and marketing organizations.
- Ability to work independently and collaboratively in a fast\-paced, highly matrixed environment.
- Willingness to travel based on client and business requirements.
Preferred Qualifications* Master’s degree or Ph.D. in Computer Science, Engineering, Data Science, Business, or a related discipline.
- Hands\-on or strategic experience with Oracle Fusion Cloud ERP, HCM, SCM, or CX .
- Experience with Oracle Fusion Data Intelligence, Oracle Analytics, and Oracle Cloud Infrastructure .
- Understanding of Oracle’s AI capabilities, including embedded and generative AI use cases across Fusion Applications.
- Experience working with other major cloud platforms and their AI/ML services, including AWS, Microsoft Azure, or Google Cloud Platform.
- Knowledge of enterprise data architecture, integration, governance, security, privacy, and responsible AI practices.
- Established external profile as a thought leader through publications, speaking engagements, industry participation, or executive advisory work.
- Experience developing business cases, value frameworks, and adoption roadmaps for enterprise AI and analytics programs.
Success in This RoleSuccess will be measured by your ability to build trusted executive relationships, influence strategic client decisions, strengthen Oracle’s position in high\-value opportunities, inform product direction, and clearly demonstrate how AI and analytics can unlock greater value from Oracle Fusion Applications.
As a Master Principal Sales Consultant you will be responsible as the expert for formulating and leading presales technical / functional support activity to prospective clients and customers while ensuring customer satisfaction. Acts as a technical resource and mentor for less experienced Sales Consultants. Focuses on large or complex sales opportunities that need creative and complex solutions. Develops productivity tools and training for other Sales Consultants. Develops and delivers outstanding Oracle presentations and demonstrations. Leads any and all aspects of the technical sales process. Advises internal and external clients on overall architect solutions.
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 Oracle, 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 $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.
Oracle AI Hiring
Oracle has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Agent Developer, Research Scientist. Positions span US, Nashville, TN, US, Santa Clara, CA, 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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