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About This Role
Lead AI Enablement Engineer
Position Summary
We are seeking a Lead AI Enablement Engineer to drive the adoption and evolution of AI-assisted engineering practices across product engineering teams. This is a hands-on individual contributor role within the Platform team, partnering closely with the Platform Lead to improve developer productivity, software quality, and delivery efficiency through practical AI enablement.
The ideal candidate combines strong software engineering fundamentals with deep, hands-on experience using AI coding tools, LLMs, and agent-based workflows. You will establish reusable AI-first development practices, integrate AI workflows into the software delivery lifecycle, and build the foundations for validated, sandboxed, and orchestrated agent execution.
You will also help shape reusable AI patterns for customer-facing product capabilities and internal employee tools, balancing innovation with security, governance, reliability, and measurable engineering outcomes.
Key Responsibilities
AI-First Engineering Enablement
- Establish and scale AI-assisted workflows across the software development lifecycle, including planning, coding, debugging, testing, design, documentation, code reviews, and production investigation.
- Develop reusable agent instructions, prompts, skills, repository context, integrations, and reference implementations that engineering teams can adopt directly.
- Evaluate emerging AI coding tools, LLM platforms, and agent frameworks to identify opportunities to improve engineering productivity and software delivery.
- Help teams transition from ad hoc, locally executed AI usage toward structured, repeatable, and validated AI-assisted workflows.
Platform Integration and Agent Orchestration
- Partner with the Platform Lead to integrate AI workflows with CI/CD pipelines, automated testing, development sandboxes, observability, release readiness, and developer productivity tooling.
- Develop patterns for orchestrating multiple AI agents and parallel workflows, with appropriate human oversight and validation.
- Build integrations with developer tools, repositories, APIs, and internal systems to provide AI agents with relevant context and controlled access to resources.
- Contribute to the evolution of sandbox-based agent execution environments that support safe, reliable, and scalable engineering workflows.
Quality, Validation, and Governance
- Design validation loops that ensure AI-generated code and other outputs meet engineering standards through automated tests, code reviews, security checks, and release controls.
- Establish evaluation methods and quality signals to assess the accuracy, reliability, usefulness, and consistency of AI-assisted workflows.
- Apply sound engineering judgment to security, privacy, access control, auditability, operational risk, and AI usage costs.
- Collaborate with engineering and platform stakeholders to ensure AI adoption improves outcomes without compromising production reliability or software quality.
Product and Internal AI Capabilities
- Define reusable architectural patterns for AI-powered product features and internal employee capabilities.
- Support the development of AI assistants, retrieval-augmented generation (RAG) solutions, knowledge agents, workflow automation, and tool-enabled applications.
- Establish shared patterns for agent integrations, context management, permissions, governance, and reusable AI components.
- Partner with product and engineering teams to identify opportunities to embed AI into existing workflows and customer-facing capabilities.
Developer Enablement and Continuous Improvement
- Drive adoption through hands-on demonstrations, pairing sessions, workshops, documentation, examples, and engineering office hours.
- Coach engineers on effective AI-assisted development practices, agent orchestration, and output validation.
- Measure the impact of AI enablement using adoption, productivity, delivery speed, software quality, developer experience, and cost metrics.
- Continuously refine workflows based on engineering feedback, evaluation results, and measurable business outcomes.
Required Qualifications
- 7+ years of software engineering experience preferred, with consideration for strong senior-level candidates who demonstrate exceptional hands-on AI engineering and enablement capabilities.
- Significant practical experience using AI tools for real-world software engineering, including AI coding assistants, agentic development workflows, and LLM-powered developer tools.
- Hands-on experience with tools such as Claude Code, Warp, or comparable AI coding and engineering platforms.
- Strong software engineering fundamentals, including application development, testing, code review, debugging, and production support.
- Experience building reusable developer workflows, integrations, automation, agent instructions, prompt patterns, skills, or reference implementations.
- Working knowledge of LLM APIs, tool calling, agent orchestration, and Model Context Protocol (MCP) or comparable integration approaches.
- Understanding of modern software delivery practices, including Git, CI/CD, automated testing, release management, and production readiness.
- Ability to evaluate AI-generated outputs critically and implement appropriate testing, review, security, and validation mechanisms.
- Strong communication and collaboration skills, with the ability to influence engineering practices and drive adoption across teams.
- A practical, AI-first mindset focused on measurable improvements in engineering productivity, delivery quality, and developer experience.
Technology and Tools
Experience with several of the following technologies and capabilities is highly desirable:
AI Engineering and Agent Development
- Claude Code, Warp, AI coding agents, and LLM-powered developer workflows.
- Agent SDKs and APIs from Anthropic, OpenAI, AWS Bedrock, or comparable platforms.
- MCP servers, tool calling, agent orchestration, reusable instructions, prompt engineering, and agent skills.
Software Engineering and Developer Productivity
- Proficiency in at least one modern programming language, such as Python, TypeScript, Go, C#, or .NET.
- Git-based development workflows, CI/CD pipelines, test automation, code review, and release management.
- Developer productivity platforms, internal tooling, and engineering workflow automation.
AI Applications and Knowledge Systems
- Retrieval-augmented generation (RAG), semantic search, embeddings, vector databases, and knowledge agents.
- AI assistants, workflow automation, and integrations with internal systems and collaboration tools.
Cloud, Infrastructure, and Observability
- AWS or comparable cloud platforms, containers, and sandboxed execution environments.
- Logging, tracing, monitoring, observability, AI evaluation, and cost tracking.
- Security controls, identity and access management, privacy, auditability, and governance for AI systems.
Preferred Qualifications
- Experience implementing sandboxed or cloud-based agent execution and parallel AI workflows.
- Familiarity with human-in-the-loop orchestration and validation of autonomous or semi-autonomous agents.
- Experience building AI-powered SaaS features, internal knowledge-base agents, Slack integrations, or employee AI assistants.
- Experience modernizing legacy applications or engineering workflows in a mature production SaaS environment.
- Domain experience in fintech, payments, accounting, eCommerce, property management, or other transactional businesses.
- Experience working in security-conscious, compliance-driven, or regulated environments.
Success Metrics
Success in this role will be measured by the ability to:
- Establish repeatable, AI-first engineering workflows that product teams can adopt across common development activities.
- Demonstrate measurable improvements in engineering productivity, delivery speed, software quality, review confidence, and developer experience.
- Improve the relevance and reliability of AI-generated outputs through better repository context, business rules, engineering standards, and validation.
- Enable engineers to orchestrate AI-driven workflows with clear testing, review, security, and release-readiness controls.
- Build a strong technical foundation for sandbox-based execution and parallel agentic development.
- Establish reusable, governed patterns for AI assistants, retrieval, automation, and internal and customer-facing AI capabilities.
- Demonstrate tangible value through adoption, quality, productivity, delivery, and cost metrics.
Work Model and Location
We are based in the New York metropolitan area and hire across the U.S. East Coast, with working hours aligned to New York time.
We value in-person collaboration while recognizing the importance of flexibility and where our employees choose to live.
- Hybrid: Candidates located near the New York metropolitan area are expected to work from the office two to three days per week.
- Remote-friendly: Candidates located farther away on the U.S. East Coast can work primarily remotely, with in-person visits approximately four to six times per year.
- Onboarding: Regardless of location, additional in-person collaboration is expected during the initial onboarding period.
This role offers the opportunity to shape how AI transforms software engineering practices across product teams while building practical, scalable solutions that deliver measurable results.
WHY JOIN US
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
EQUAL OPPORTUNITY EMPLOYER: Matrix is an Equal Opportunity Employer and Prohibits Discrimination and Harassment of Any Kind. Matrix is committed to the principle of equal employment opportunity for all employees, providing employees with a work environment free of discrimination and harassment. All employment decisions at Matrix are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion or belief, family or parental status, or any other status protected by the laws or regulations in our locations. Matrix will not tolerate discrimination or harassment based on any of these characteristics. Matrix encourages applicants of all ages*.*
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Matrix Global, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
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.
Matrix Global AI Hiring
Matrix Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jersey City, NJ, 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/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 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).
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 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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