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About This Role
Meet the Moment with Alteryx
We're living through a once-in-a-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision - and Alteryx is leading the transformation.
You'll be working on the challenges that sit at the heart of modern business. No matter your role, the work you do will help organizations move faster, see more clearly, and tackle questions that used to feel impossible.
If you're ready to meet the moment with innovation, curiosity, and excellence, there's a place for you here.
Alteryx is searching for a Sr. Architect, Professional Services - Agentic AI Solutions . This position is remote-friendly.
Position Overview:
The Senior Architect, Professional Services - Agentic AI Solutions is a highly technical, customer-facing
enterprise AI/ML architect responsible for designing, developing, and guiding production-ready Agentic AI solutions across the Alteryx ecosystem and broader enterprise technology landscape.
As part of a new Professional Services team focused on Agentic AI development and deployment, this role partners directly with enterprise customers to move from discovery and solution architecture through prototyping, development, deployment, and operationalization. The team will place particular emphasis on Office of Finance use cases and regulated business environments.
Senior Architects work closely with Sales, Solutions Engineering, Professional Services, and customer
stakeholders to translate business needs into secure, scalable AI solutions that deliver measurable
outcomes while meeting enterprise requirements for governance, control, reliability, and adoption.
Primary Responsibilities:
- Design, develop, and guide deployment of Agentic AI solutions for enterprise customers, with particular emphasis on Office of Finance use cases and workflows.
- Translate business, data, process, security, and control requirements into scalable enterprise AI/ML architectures and implementation plans.
- Prototype and implement AI agents and agentic workflows that connect large language models, enterprise data, APIs, tools, applications, and automation platforms.
- Design and deploy Agentic AI solutions that leverage and integrate with Alteryx Designer, Server, and the Alteryx One enterprise analytics platform, alongside modern cloud data platforms, AI/ML services, and enterprise systems of record.
- Establish production-ready patterns for human-in-the-loop controls, access management, evaluation, observability, auditability, reliability, and responsible AI governance.
- Partner with customer technical and business stakeholders to define success criteria, validate solution feasibility, and guide solutions from proof of concept into production.
- Partner with Sales and Solutions Engineering to shape strategic AI opportunities, validate architecture and delivery feasibility, and connect technical solutions to customer business outcomes.
- Identify opportunities to expand adoption and value by proactively recommending additional AI, automation, and data use cases.
- Create customer-facing architecture documentation, implementation guidance, reusable solution patterns, and technical enablement materials.
Share technical knowledge internally and contribute to emerging Agentic AI standards, delivery practices, and accelerators across Professional Services. *
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field, or equivalent practical experience.
- 5+ years of experience in a customer-facing technical role such as Solutions Architect, AI/ML Architect, Consultant, Technical Account Manager, Presales Engineer, or similar role.
- 3+ years of experience in AI/ML enterprise architecture with a focus on Agentic AI development and deployment.
- Demonstrated experience designing and implementing enterprise AI, data, analytics, or automation solutions.
- Experience working directly with enterprise customers and navigating complex stakeholder environments.
- Strong working knowledge of Alteryx Designer, Server, and the Alteryx One enterprise analytics platform is strongly preferred. Experience architecting and deploying solutions across comparable enterprise analytics platforms will also be considered.
- Experience supporting Office of Finance, accounting, banking, SOX-compliant environments, or similar financial services and regulated business use cases is preferred.
- Excellent communication, problem-solving, and stakeholder management skills across technical and business audiences.
Ability to travel up to 20%. *
Technical Expertise:
- Hands-on experience with enterprise AI/ML architectures and Agentic AI development, including AI agents, agent orchestration, tool use, large language models (LLMs), retrieval-augmented generation (RAG), and API-based integrations.
- Experience moving AI solutions from prototype or proof of concept into secure, scalable, production-ready deployments.
- Strong understanding of AI governance, security, evaluation, observability, model and prompt lifecycle management, and human oversight patterns.
- Experience integrating AI solutions with enterprise data platforms and cloud ecosystems such as Google Cloud Platform (GCP), AWS, Azure, Snowflake, Databricks, or similar technologies.
- Working knowledge of modern software and data engineering practices, including Python or similar languages, APIs, version control, CI/CD, and cloud-native deployment patterns.
Ability to communicate complex AI concepts and architecture decisions clearly to both technical and business audiences. *
Compensation:
Alteryx is committed to fair, equitable, and transparent compensation. Final compensation will be determined by various factors such as your relevant work experience, education, certifications, skills, and geographic location.
The salary range for this role in the United States is $121,375 - $132,190.
In addition, you may be eligible for additional compensation, such as a bonus. Employees may also be eligible for a wide range of other benefits, including medical, retirement, financial, wellness, time off, employee discounts, and others.
Interested? Learn more and apply today at alteryx.com/careers!
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#LI-REMOTE
Find yourself checking a lot of these boxes but doubting whether you should apply? At Alteryx, we support a growth mindset for our associates through all stages of their careers. If you meet some of the requirements and you share our values, we encourage you to apply. As part of our ongoing commitment to a diverse, equitable, and inclusive workplace, we’re invested in building teams with a wide variety of backgrounds, identities, and experiences .
Benefits & Perks:
Alteryx has amazing benefits for all Associates which can be viewed here .
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Alteryx will consider for employment qualified applicants with arrest and conviction records.
This position involves access to software/technology that is subject to U.S. export controls. Any job offer made will be contingent upon the applicant’s capacity to serve in compliance with U.S. export controls.
Salary Context
This $121K-$132K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Alteryx, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($126K) sits 40% below the category median. Disclosed range: $121K to $132K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Alteryx AI Hiring
Alteryx has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $132K - $236K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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