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About This Role
Senior Architect / AI Context Engineer
This role is to be based near one of our offices in New York City, Austin, or Los Angeles. (Hybrid)
About Us
We drive intelligent growth for ambitious businesses and leading brands.
Customer understanding is more potent and drives greater value when insights go to work directly within marketing and experience priorities. To give our clients that advantage, we put an insights-driven operating system at the core of our design, go-to-market, and digital experience solutions.
Material clients make smarter growth choices because they can see clearly how and when to take the shots that count. Together, we connect consumer intelligence with customer demand generation to build lasting, profitable relationships.
About the Senior Architect / AI Context Engineer Role
The Senior Architect / AI Context Engineer is a strategic generalist who operates at the intersection of enterprise architecture, AI operationalization, and context engineering. This role is responsible for authoring governance artifacts, designing agent and tool-server architectures, building reusable accelerator patterns, curating agent skills and prompt harnesses, and driving the AI-native design agenda across the organization. This is the role that bridges the gap between strategic architecture decisions and hands-on AI implementation.
What You'll Do
- Design, author, and maintain MCP (Model Context Protocol) server architectures across recurring patterns: knowledge and memory servers, platform API bridge servers, and event- or webhook-triggered agents, following established AI-native design principles.
- Build and curate reusable agent skills, system prompts, scoring rubrics, and harness configurations for a library of governed agent archetypes.
- Author and publish enterprise governance documentation: Architecture Decision Records (ADRs), security review checklists, tiered governance model documentation, and architecture review templates for the enterprise technology intake and approval pipeline.
- Design and implement an enterprise context engineering layer: repo-level context file standards (AGENTS.md / CLAUDE.md conventions), templates, and harness engineering patterns.
- Drive execution of a governed internal agent platform pilot: author agent archetypes, configure governed sessions with scoped tool access, and validate end-to-end audit trail flows on a managed agent runtime.
- Develop and maintain an accelerator solution map: reusable toolkit pattern definitions, capability gap analysis, and accelerator specifications spanning all service lines.
- Support Enterprise Knowledge Graph scoping: ontology design, data catalog foundation architecture, and schema governance, in collaboration with data science and commercial strategy teams.
- Author SDLC Standards and Engineering Operating Model artifacts: quality gates, PR/review standards, Definition of Done/Ready templates, CI/CD control specifications.
- Produce EA leadership artifacts: Enterprise Capability Maturity Map, Technology Demand and Decision Flow Dashboard, Application/Tool Portfolio Map, cross-lane integration maps, and an AI Readiness Scorecard.
- Operate as the primary technical liaison between the enterprise architecture function and service line teams, translating strategic architecture intent into actionable engineering guidance.
About You
- 8+ years in software architecture, solutions architecture, or enterprise architecture roles.
- Demonstrated experience designing and building AI/LLM-integrated systems: agent architectures, prompt engineering, RAG pipelines, tool-use patterns.
- Strong understanding of context engineering: structured prompt design, system prompt authoring, knowledge retrieval optimization, context window management.
- Experience with at least two of: MCP (Model Context Protocol), LangChain/LangGraph, Claude API, OpenAI API, or equivalent LLM orchestration frameworks.
- Proficiency in multiple programming languages (Python, TypeScript/Node.js required; others a plus).
- Proven track record authoring governance artifacts: ADRs, architecture standards, SDLC documentation, security review frameworks.
- Experience with enterprise platform ecosystems: Atlassian (Confluence, Jira), Azure, GitHub, Salesforce.
- Strong technical writing and documentation skills; able to produce executive-ready architecture briefs and technical specifications simultaneously.
Preferred Qualifications
- Hands-on experience with a major agentic AI ecosystem: model providers' agent runtimes, skill and tool definitions, MCP servers, and managed or hosted agent platforms.
- Background in market research, consulting, or insights/analytics industries.
- Experience with knowledge graph design, ontology modeling, or semantic data architectures.
- Familiarity with institutional memory and knowledge management platforms, particularly internal developer tooling that exposes organizational context to AI agents.
- A grounded perspective on AI adoption: recognizing that velocity bottlenecks in AI-enabled delivery are typically process habits and organizational culture rather than tooling capability.
- Experience with enterprise governance models (TOGAF, Zachman, or custom tiered governance).
Why Work for Material?
- Material is a global company and we work with leading brands worldwide. We create and launch new brands and products, putting innovation and value creation at the center of our practice. Our clients are leaders in their class, across sectors from technology to retail, transportation, finance, and healthcare.
- Material employees join a peer group of exceptionally talented colleagues across the company, the country, and the world. We develop capabilities and leading-edge market offerings across seven global practices including strategy and insights, design, data & analytics, technology, and tracking.
- A community focused on learning and making an impact. Material is an outcomes-focused company. We create experiences that matter, create new value, and make a difference in our clients’ lives and their customers’ lives.
Pay Range: $150,000.00 – 180,000.00
The range shown represents a grouping of relevant ranges currently in use at Material. Actual range for this position may differ, depending on location and specific skillset required for the work itself.
Equal Employment Opportunity
All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other factors prohibited by law.
Privacy Statement
Material is committed to protecting privacy in our recruiting processes for all candidates. For more information, please refer to our Privacy Policy. California-resident applicants should also refer to our California-resident Candidate Privacy Statement.
If you need support with a privacy-related matter, please send an email to: [email protected]
Salary Context
This $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 MATERIAL, 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. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $150K to $180K.
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.
MATERIAL AI Hiring
MATERIAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 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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