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About This Role
Job Description
Our talented Integrated Center of Excellence Data \& AI team is made up of globally recognized experts – and there’s room for more analytical and ambitious data professionals. If you’re passionate about helping clients transform their business with the latest data, analytics, and AI technologies, let’s talk.
Come join us
As an Intelligent Data Platform (IDP) Solution Architect within our Integrated Center of Excellence Data \& AI Practice, you'll leverage a client\-centric approach to understand our client’s challenges and opportunities to deliver solutions and services that make transformative change and deliver high value business outcomes , you will play a senior leadership role in shaping, selling, and delivering enterprise ‑ scale analytics platforms built on Microsoft Fabric and contribute to shape the strategic direction using data platforms, reporting, self\-service analytics, real time intelligence and AI engineering
You will engage in strategic solutioning, lead high ‑ impact pre ‑ sales pursuits, intervene in complex or high ‑ risk deliveries with enterprise data solutions leveraging Microsoft Azure, Fabric and Databricks and help define Avanade ’ s Fabric ‑ led data platform strategy, offerings, and accelerators. This role combines deep technical leadership, client advisory, and platform stewardship, as a trusted advisor with a strong partnership focus with Microsoft.
Using the power of Microsoft Fabric ( OneLake , Data Factory, Spark, Warehousing, Real ‑ Time Analytics, and Power BI), alongside Azure services and applied AI, we help clients unlock insights, modernise analytics estates, and drive measurable business outcomes.
Together we do what matters
What you’ll do
- Lead pre ‑ sales, solution planning, and solution design for Microsoft Fabric ‑ centric sales pursuits and strategic accounts.
- Shape deals and propositions, including architecture, estimates, commercial inputs, and delivery approach, in collaboration with sales and delivery leadership.
- Act as a trusted advisor to senior client stakeholders, translating complex Fabric capabilities into clear business value and outcomes.
- Provide technical and delivery assurance, addressing delivery risks, quality concerns, and architectural issues when required .
- Drive Microsoft partnership engagement at an offering and capability level, influencing roadmap alignment and go ‑ to ‑ market activities.
- Define and promote best practices for Microsoft Fabric architecture, governance, security, performance, and cost optimisation.
- Establish reference architectures, accelerators, and reusable patterns across Fabric workloads (Lakehouse, Warehouse, Real ‑ Time, Semantic Models).
- Contribute thought leadership, points of view, and IP to support global GTM activities.
- Coach, mentor, and inspire technical leaders and architects across regions, building a strong Fabric community of practice.
- Stay current with Fabric and Azure platform evolution, incorporating new capabilities into client solutions and internal offerings.
- Support internal asset development and engineering initiatives to strengthen Avanade’s Fabric differentiation.
Qualification
Skills and experiences
- Management Consulting or Systems Integrator experience strongly preferred.
- Proven experience architecting and delivering enterprise analytics solutions using Microsoft Fabric, including OneLake , Data Factory, Spark, Warehousing, Real ‑ Time Analytics, and Power BI.
- Strong understanding of data platform governance, including Microsoft Purview, security, lineage, and metadata management.
- Hands ‑ on experience with data pipeline design, ELT/ETL, data modelling, and analytics architectures.
- Experience leading performance tuning, capacity management, and cost optimisation within Fabric environments.
- Strong stakeholder management and executive ‑ level communication skills.
- Demonstrated ability to build reusable frameworks, reference architectures, and accelerators.
- Experience leading and mentoring senior technical teams and communities.
- Proven success building and maintaining strategic partnerships with Microsoft.
About you
- Passionate about technology and advising clients at a strategic level.
- Comfortable operating across technical, commercial, and delivery leadership responsibilities.
- Experienced working with large, complex enterprise accounts and distributed delivery teams.
- Able to orchestrate and influence global teams to deliver successful 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 03/19/2026 and open for at least 3 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
Role Location Annual Salary Range
California 155,000\- 184,000
Cleveland 140,000\- 165,000
Colorado 140,000\- 165,000
District of Columbia 155,000\- 184,000
Illinois 150,000\- 178,000
Maryland 155,000\- 184,000
Massachusetts 155,000\- 184,000
Minnesota 150,000\- 178,000
New York 165,000\- 195,000
New Jersey 140,000\- 165,000
Washington 155,000\- 184,000
Salary Context
This $140K-$184K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Avanade, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $140K to $184K.
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 Architect ($292,900). 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.
Location Context
AI roles in Seattle pay a median of $223,600 across 678 tracked positions. That's 22% 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 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).
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 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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