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About This Role
*Our goal is to solve problems and deliver results for our clients. At Cloud and Things, you can be a part of transforming the public sector’s IT environment. Our team is on the forefront of helping to solve the government's most complex IT challenges. If you are seeking a role that offers the opportunity to work on rewarding projects, consider a career with Cloud and Things.?*
*\*This is an exempt position. Salary commensurate with experience\**
Job Title: Data / AI Architect
Location: NY, NY (Onsite)
Duration: 6 Months (with potential for extension)
Start Date: Estimated \- 9/14/2026
W2 Hourly Rate: $50 \- $60/hr
Application Deadline: 8/21/2026
Overview:
We are seeking a Data \& AI Architect who will support our NYC client.
This hands\-on architecture role will partner with business and operational stakeholders to turn ambiguous data and AI needs into approved, production\-ready solutions. The architect will define secure, scalable enterprise architectures across data, AI, cloud, and database platforms while guiding delivery teams through implementation.
Duties:
- Engage stakeholders across MTA departments to identify, clarify, and shape emerging data and AI use cases into well\-scoped opportunities.
- Translate business needs into clear, actionable requirements and solution specifications for technical and non\-technical audiences.
- Present proposed architectures, trade\-offs, and business value to stakeholders and governance bodies and drive requirements through formal approval.
- Architect end\-to\-end data and AI solutions spanning ingestion, storage, transformation, modeling, serving, and consumption layers.
- Design and implement solutions using agentic development and AI\-assisted tooling while maintaining quality, security, and maintainability.
- Define reference architectures, patterns, and standards for data and AI across multi\-cloud and multi\-database environments.
- Ensure solutions meet enterprise requirements for security, privacy, data governance, cost efficiency, and regulatory compliance.
- Collaborate with engineering, data science, platform, and product teams to move approved solutions from design into production.
- Mentor engineers and analysts and promote strong enterprise data, AI, and architecture practices across the organization.
Mandatory Qualifications:
- 8\+ years of overall experience in data engineering, data architecture, software engineering, or a closely related field
- 6\+ years experience in an architecture\-focused role.
- Hands\-on design and delivery experience across at least two major cloud providers, such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).
- Experience across relational, NoSQL, analytical/warehouse, and vector or graph database technologies.
- Practical experience with multiple AI/ML and generative AI frameworks, models, and platforms, including LLMs, ML pipelines, RAG, and model orchestration.
- Strong requirements\-gathering and stakeholder\-facing experience, including the ability to clarify ambiguous needs and produce actionable specifications.
- Excellent written and verbal communication skills with the ability to explain complex technical concepts to non\-technical audiences and secure stakeholder buy\-in.
- Hands\-on experience delivering solutions with agentic development tooling and AI\-assisted development workflows.
- Demonstrated experience taking solutions from concept and approval through production implementation.
Desirable Qualifications:
- Recent primary experience with the Microsoft Azure stack, including Azure data services, Microsoft Fabric or Synapse, Azure OpenAI, and Azure Machine Learning.
- Hands\-on development experience with Microsoft Copilot tooling, such as GitHub Copilot, Copilot Studio, or Microsoft 365 Copilot.
- Experience working in large, complex, public\-sector, transit, or infrastructure organizations.
- Relevant cloud or data architecture certifications.
- Familiarity with enterprise data governance, MLOps, and responsible AI practices.
*Ready to make a difference?*
*We’re eager to connect with qualified candidates committed to delivering results and fostering excellence within client projects.*
*Cloud and Things complies with all applicable federal, state, and local laws regarding recruitment and hiring. All qualified applicants are considered for employment without regard to race,?color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other category protected by applicable federal, state, or local laws.*
AI\-Assisted Resume Evaluation Notice
Cloud and Things – Talent Management
Notice to Candidates
Cloud and Things utilizes artificial intelligence (AI) tools to assist our recruiting team in evaluating candidate applications for streamlining; consistency, efficiency, and thoroughness. All hiring decisions are ultimately made by our human recruiting professionals.
How AI Is Used
Our AI tools assist by:
- Analyzing resumes against job requirements
- Supporting our recruiters in candidate data evaluation
- Ensuring consistent review standards across all applications
- Important: AI serves as a support tool only. As noted above, all candidate selection and hiring decisions are made by experienced human recruiters. Your unedited resume will be processed by our AI tools as part of this evaluation.
Your Data and Privacy
Cloud and Things Data Handling:
- Your information is processed securely and used exclusively for recruitment purposes
- Cloud and Things may store your resume in our Applicant Tracking System (ATS) indefinitely for future job matching opportunities
- You may opt out of long\-term ATS storage by emailing your name and your request to opt out of storing your resume in the ATS to: [email protected]
- All personal information is handled confidentially in accordance with our privacy policy
AI Tool Data Processing:
- AI processing data is retained for a maximum of 90 days, after which it is deleted
- All data sent to AI tools is encrypted in transit and at rest
- AI tools comply with applicable privacy laws including GDPR and CCPA
- Personal data is anonymized or minimized wherever possible during AI processing
Your Participation
By submitting your application, you acknowledge this notice and consent to AI\-assisted evaluation as part of our recruitment process. You may opt out only by choosing not to submit your resume for consideration.
Salary Context
This $104K-$124K range is in the lower quartile for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Cloud and Things, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($114K) sits 52% below the category median. Disclosed range: $104K to $124K.
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.
Cloud and Things AI Hiring
Cloud and Things has 1 open AI role right now. They're hiring across AI Architect. Based in New York, NY, US. Compensation range: $124K - $124K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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