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About This Role
Job Description
The Senior Manager, AI Architect (GenAI \& Azure) leads the solution design, architecture, and scaled enablement of enterprise AI solutions, serving as a trusted advisor guiding clients from pilot to scaled value realization. This is a client\-facing, architecture\-led consulting role—focused on AI solution design and Gen AI technical leadership.
You will partner globally by shaping pursuits, defining target\-state AI architectures, and enabling program execution across complex engagements. Partnering with sales, solutioning, and delivery leaders, you will design and architect enterprise grade AI architectures using Azure AI Services, Azure OpenAI, Azure AI Search, and Copilot Studio, integrating patterns such as RAG, vector stores, CLU/NLP, conversational AI, and emerging Agentic AI. You will advise on Responsible AI, security, privacy, and Azure Well\-Architected principles; define operating and governance models; and ensure solutions meet cost, risk, and compliance requirements.
Core responsibilities include:
- Client Leadership \& Trusted Advisory: Build stakeholder relationships, lead executive briefings, shape AI roadmaps, and translate strategy into well\-architected end\-to\-end AI and GenAI solutions aligned to client business strategy and measurable outcomes
- Architecture Ownership: Define end\-to\-end solution blueprints (data, integration, security, model lifecycle), establish standards for RAG/orchestration, and guide Copilot/agent strategies.
- Sales \& Growth: Lead pre\-sales architecture for pursuits, create PoVs and reference designs, and differentiate with assets/accelerators and industry patterns.
- Delivery Oversight: Govern architectural integrity across programs, run design reviews, manage risks and trade\-offs, and ensure value tracking and adoption.
- Practice Building: Mentor architects/engineers, upskill teams on GenAI and Responsible AI, and contribute to IP, playbooks, and reusable components.
Qualification
Job Qualifications
- Consulting Experience: 10\+ years in consulting leading complex, client\-facing technology programs; 3–5\+ years in cloud architecture; 2\+ years architecting AI/GenAI solutions (strategy \& design, not primary development).
- Azure \& GenAI Expertise: Deep experience with Azure AI Services, Azure OpenAI, Azure AI Search, Copilot Studio, and data/integration patterns; hands\-on architecture with RAG, vector databases, orchestration, conversational AI, NLP/CLU, and an understanding of Agentic AI trajectories.
- Enterprise Architecture: Strong grasp of security, identity, data governance, compliance, and Azure Well\-Architected principles; familiarity with Responsible AI frameworks and model risk management.
- Client Leadership: Proven trusted advisor to senior business/technology leaders; excels at storytelling, facilitation, and negotiation; can quantify value (KPIs/OKRs, ROI/TEI) and drive executive alignment.
- Sales \& Delivery: Track record shaping pursuits (solutioning, estimations, commercials), leading design workshops, overseeing delivery quality, and de\-risking complex programs.
- Operating Model \& Governance: Experience defining AI operating models, platform strategy, SDLC/MLOps/LLMOps guardrails, data access patterns, and change/adoption plans.
- Team Leadership: Mentors cross\-functional teams; builds capability; contributes to assets, accelerators, and reference architectures.
- Education \& Certifications: Bachelor’s in technology or business (or equivalent experience). Azure certifications (e.g., Azure Solutions Architect Expert, Azure AI Engineer Associate) preferred.
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 3/20/2026 and open for at least 10 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:
U.S. Employee Benefits \| Avanade
California
$180,000 – 205,000
Cleveland
$165,000 \- 195,000
Colorado
$165,000 \- 195,000
District of Columbia
$185,000 \- 220,000
Illinois
$172,000 \- 205,000
Maryland
$176,000 \- 210,000
Massachusetts
$176,000 \- 210,000
Minnesota
$172,000 \- 205,000
New York / New Jersey
$185,000 \- 220,000
Washington
$185,000 \- 220,000
At Avanade, we are committed to ensure our people feel appreciated and empowered to succeed both personally and professionally.
Salary Context
This $172K-$220K range is below the median for AI Architect roles in our dataset (median: $225K across 99 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 26,159 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 $292,900 based on 108 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($196K) sits 33% below the category median. Disclosed range: $172K to $220K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Avanade AI Hiring
Avanade has 4 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Atlanta, GA, US, Seattle, WA, US, Chicago, IL, US. Compensation range: $184K - $230K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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