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About This Role
Job Description
The AI Architect Manager is a critical leadership role responsible for designing, developing, and scaling enterprise AI solutions aligned to business strategy and measurable outcomes. This role serves as a trusted advisor and subject matter expert, enabling clients to establish, mature, and scale their AI programs across the Microsoft ecosystem.
The incumbent will lead AI architectural design and technical solutioning efforts by evaluating business processes, operating models, and application architectures to deliver scalable, secure, and high\-performing AI solutions tailored to client environments. This role is responsible for translating complex business requirements into actionable AI architectures that deliver tangible value.
The AI Architect Manager will partner closely with cross\-functional stakeholders and global teams across Avanade, Accenture, Microsoft, and partner ecosystems to ensure a cohesive, enterprise\-wide approach to AI solution delivery, innovation, and capability development.
This position requires a strategic and hands\-on leader capable of operating effectively in a complex, matrixed environment while driving innovation, collaboration, and high\-quality outcomes for clients.
Key Responsibilities
Provide leadership and oversight for AI solution architecture, ensuring alignment with client business strategies, technical requirements, and industry best practices.
Translate business requirements into AI solution scope, value propositions, and scalable architectural designs in collaboration with AI teams and Solution Architects.
Analyze client processes, systems, and data landscapes to identify AI transformation opportunities and recommend appropriate approaches based on client maturity.
Lead solutioning activities including estimation, technical design, and proposal development across the pre\-sales and delivery lifecycle.
Deliver AI engagements in roles including AI SME, Solution Architect, or Technical Architect, ensuring high\-quality outcomes.
Provide governance, quality assurance, and best practice guidance across AI project teams related to delivery approach, design, and implementation.
Facilitate client workshops, stakeholder meetings, and pre\-sales presentations to articulate AI value and solution approaches.
Develop and contribute to AI assets, accelerators, and methodologies to enhance organizational capability and delivery efficiency.
Collaborate across global teams and partner ecosystems to share knowledge, drive innovation, and scale AI capabilities.
Priority Skill
Agentic AI Solution Architecture
Job Requirements
Must be able to work nights, weekends, and holidays as operational needs dictate
Travel including internationally, as needed
IMPORTANT before applying for a job internally:
Review this application criteria.
You must already possess the appropriate visas and/or work permits required to live and work in the country the job is based. Do not apply until you have confirmed these authorizations are in place, or the job description states that international applicants will be considered.
If you apply and are selected for a job at a higher level, it will be offered at your current level. You will be considered for promotion at the next eligible promotion period, except where subject to local law or contractual obligations.
Qualification
Basic Qualifications
Minimum of 5–7 years of experience in AI, Data, or consulting roles.
5 years’ experience designing and delivering enterprise AI solutions aligned to business outcomes.
5 years’ experience with Microsoft AI ecosystem, including Azure AI Services, Azure OpenAI, Azure AI Foundry, and Azure Machine Learning.
5 years’ experience developing or supporting Agentic AI and Generative AI solutions.
5 years’ experience Python for analytics and software engineering, including experience with modern AI libraries (e.g., LangChain, DSPy).
5 years’ experience applying data and analytics to solve business problems, including data preparation and pipeline development.
5 years’ experience with DevOps, MLOps, SDLC, and Agile development practices.
5 years’ experience working in client\-facing environments, including stakeholder engagement and solution delivery.
Bachelor’s degree in computer science or quantitative field experience or equivalent experience (Minimum 12 years of experience with no degree or 6 years with an Associate’s Degree).
Preferred Qualifications
Microsoft Certified AI Engineer or equivalent certification.
Experience in management consulting and/or systems integration environments.
Strong background in software engineering, data engineering, or solution architecture.
Experience with Azure Databricks and distributed data processing environments.
Knowledge of real\-time and streaming data analytics, time\-series analytics, and system integration patterns.
Strong understanding of data science and machine learning principles.
Experience operating across multiple architectural domains, including enterprise and infrastructure architecture.
Ability to articulate AI use cases across multiple industries (e.g., Manufacturing, Healthcare, Retail, Resources).
Strong communication, stakeholder management, and client\-facing skills with the ability to influence decision\-making.
Demonstrated ability to write high\-quality, production\-ready code.
Ability to think strategically while breaking down complex problems into scalable architectural components.
Comfortable operating in a hands\-on role within a fast\-paced, global environment.
Professional Skills
Highly organized with strong problem\-solving and analytical capabilities.
Ability to collaborate effectively across diverse, matrixed teams and global environments.
Strong communication and interpersonal skills with the ability to build trust and credibility with stakeholders.
Demonstrated ability to operate both strategically and tactically, balancing big\-picture thinking with technical execution.
Commitment to innovation, continuous learning, and delivering high\-quality client outcomes.
Compensation at Avanade varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Avanade provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted on 07/08/2026 and open for at least 30 days.
Avanade offers a market competitive suite of benefits including medical, dental, vision, life, and long\-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off.
See more information on our benefits here: Benefits and Total Rewards \| Avanade United States
California \- $150,400 \- $184,300
Cleveland \- $139,200 \- $165,300
Colorado \- $139,200 \- $165,300
District of Columbia \- $155,200 \- $184,300
Illinois \- $150,400 \- $178,600
Maryland \- $155,200 \- $184,300
Massachusetts \- $155,200 \- $184,300
Minnesota \- $139,200 \- $165,300
New York / New Jersey \- $164,800 \- $195,700
Washington \- $155,200 \- $184,300
Salary Context
This $150K-$184K range is below the median for AI Architect roles in our dataset (median: $197K across 33 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 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Avanade, 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 $254,798 based on 67 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 34% below the category median. Disclosed range: $150K to $184K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Avanade AI Hiring
Avanade has 1 open AI role right now. They're hiring across AI Architect. Based in Chicago, IL, US. Compensation range: $184K - $184K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national median.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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