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USA · Full\-time · Management
#### About The Position
Reporting directly to the CEO, the Chief Technology Officer (AI \& Platform) will lead the company’s end\-to\-end technology organization, including AI systems, platform architecture, engineering operations, and data infrastructure.
This is a highly strategic yet deeply hands\-on leadership role requiring strong expertise in software engineering, AI\-native architectures, enterprise platform development, and organizational leadership. The ideal candidate will combine visionary thinking with the ability to work closely alongside high\-performing engineering teams to rapidly design, build, and scale innovative AI\-powered products.
We are redefining the future of the research and advisory industry through AI\-driven innovation and next\-generation intelligence platforms. This is a unique opportunity to build transformative technology from the ground up and shape a platform that will empower thousands of innovators across leading global enterprises.
Key Responsibilities
Technology Strategy \& Architecture
- Define and drive the company’s overall technology strategy aligned with long\-term business objectives
- Lead architecture decisions across:
- AI platforms and intelligent systems
- Enterprise applications and APIs
- Data infrastructure and analytics platforms
- Cloud\-native systems and internal tooling
- Establish scalable technical foundations that support rapid innovation and execution
- Evaluate and select technologies, frameworks, vendors, and infrastructure platforms
AI Platform \& Product Innovation
- Lead the development of next\-generation AI\-native intelligence platforms
- Architect advanced AI systems including:
- Knowledge and reasoning architectures
- Multi\-step reasoning systems
- Intelligent workflow automation
- Scenario modeling and decision\-support capabilities
- Drive innovation in AI\-powered user experiences that improve customer engagement, adoption, and business outcomes
- Partner closely with product and design teams to transform complex technical capabilities into intuitive enterprise solutions
Engineering Operations \& Delivery
- Champion an AI\-assisted software development lifecycle leveraging automation, AI agents, and modern engineering workflows
- Build and maintain highly automated CI/CD pipelines to improve development velocity, reliability, and quality
- Ensure platform scalability, resilience, observability, and operational excellence
- Establish and maintain security and compliance standards, including OWASP and SOC2 best practices
- Lead modern software engineering processes, release management, and platform governance
Executive \& Cross\-Functional Leadership
- Collaborate closely with Product, Sales, Customer Success, and executive leadership teams
- Support enterprise customer engagements, strategic partnerships, and key client initiatives
- Serve as a core member of the executive leadership team, helping shape company direction and growth strategy
- Align technology investments and roadmap priorities with evolving business needs
Engineering Organization \& Team Leadership
- Build, scale, and mentor high\-performing engineering organizations
- Foster a collaborative, innovative, and growth\-oriented engineering culture
- Define organizational structures, hiring strategies, and capability development plans
- Establish leadership pipelines, performance management frameworks, and engineering best practices
- Lead geographically distributed teams effectively across multiple regions and functions
Qualifications \& Experience
Technical Leadership
- Proven experience leading architecture and engineering for enterprise\-scale SaaS platforms
- Strong hands\-on background in software development, cloud\-native systems, and AI technologies
- Deep understanding of:
- AI and machine learning platforms
- Data analytics and intelligent systems
- Distributed systems architecture
- APIs, microservices, and event\-driven systems
- Modern software engineering practices and frameworks
- Experience integrating AI capabilities into both products and software development lifecycles
Leadership \& Execution
- Demonstrated success scaling software engineering organizations and leading complex technology transformations
- Ability to operate effectively at both strategic and execution levels
- Strong product mindset with focus on delivering measurable business value
- Excellent communication and stakeholder management skills across technical and non\-technical audiences
- Comfortable navigating ambiguity, rapid growth, and startup\-like environments
Personal Attributes
The ideal candidate:
- Is passionate about building transformative technology with meaningful impact
- Thrives in fast\-paced, high\-growth environments
- Combines creativity, innovation, and pragmatic execution
- Leads with humility, collaboration, and accountability
- Inspires and empowers teams through servant leadership
- Enjoys mentoring, coaching, and developing people
- Brings curiosity, bold thinking, and a strong sense of ownership
Education
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field required
- Master’s degree preferred
- Broad knowledge of modern software engineering, AI technologies, cloud infrastructure, frameworks, and industry trends
- Experience leading large\-scale organizational and technical transformation initiatives across globally distributed teams
Matrix is a global, dynamic, and fast‑growing technology services and consulting company with over 17,000 employees worldwide. Founded in 2001, Matrix has grown through significant organic expansion and strategic acquisitions, executing some of the largest and most impactful technology projects in the market.
We specialize in the design, development, and implementation of advanced technologies, software solutions, and digital products. Our services include infrastructure and consulting, IT outsourcing and offshore delivery, training and assimilation programs, and acting as a trusted implementation and delivery partner for the world’s leading software vendors.
With deep expertise across both the private and public sectors—spanning Finance, Telecom, Healthcare, Hi‑Tech, Education, Defense, and Security—Matrix serves a strong customer base in Israel alongside a continuously expanding global client portfolio.
At Matrix, our people are at the heart of our success. We are a community of talented, creative, and dedicated professionals who are passionate about innovation and excellence. We actively attract, develop, and retain top talent, recognizing that every employee’s contribution is critical to our continued growth and future success.
Matrix’s success is built on a challenging and engaging work environment, competitive compensation and benefits, and meaningful career development opportunities. We foster a diverse, inclusive culture that encourages collaboration, continuous learning, and growth—together.
Join the winning team. Be challenged, grow your career, and enjoy the journey in a highly respected global organization.
To learn more, visit: www.matrix\-ifs.com
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Matrix International Financial Services, 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 $181,170 based on 12,692 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Matrix International Financial Services AI Hiring
Matrix International Financial Services 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 $215,300 across 523 tracked positions. That's 8% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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