Interested in this AI Architect role at SPR?
Apply Now →Skills & Technologies
About This Role
Data AI Architect
Information Technology
Job Number:
4936
City :
State :
DATA AI ARCHITECT
WHO IS SPR?
SPR helps companies implement the right technology that helps them balance users’ expectations today while planning for tomorrow’s business demands. A technology modernization firm, SPR works together with clients to develop or modernize digital products and platforms.
We’re 150\+ strategists, developers, designers, architects, consultants, thinkers, and doers in Chicago and Milwaukee. We work with 100\+ mid\- to enterprise\-size clients across industries like professional services and manufacturing. We think about the end users and rigorously apply the latest technologies and frameworks to address our clients’ needs. Specializing in custom software development, cloud, data, and user experience solutions, SPR promises to Deliver Beyond the Build by providing proactive advice, sharing knowledge, responding to change in an agile way, and investing time to deeply understand our clients’ business.
We operate in a fun, casual work environment and have great benefits including competitive salary, bonuses, generous vacation time, big fitness incentives, and medical/dental/vision insurance. By joining the SPR team, you’ll be problem solving, working hard and making an impact through your projects – and you’ll be part of a unique culture and rewarded for it.
WHAT IS THE POSITION?
SPR is seeking an AI Data Architect to join our practice as a senior technical leader working for clients across our portfolio. This is a hands\-on role for someone who can own data and AI architecture decisions, lead small teams of engineers, and translate complex client needs into scalable, production\-grade solutions.
The right candidate operates at the intersection of data architecture and AI platform engineering: designing the data layer that feeds generative and agentic AI systems, enforcing governance and quality standards, and building the pipelines and integrations that make AI solutions reliable in production. This is not a strategy\-only role. SPR's architects write code, make and defend architecture decisions, and deliver alongside the teams they lead.
This person will represent SPR at the client level, manage expectations with stakeholders up to the executive level, and contribute to SPR's internal practice through thought leadership, sales support, and candidate interviewing.
RESPONSIBILITIES
\| Design and maintain sustainable data architecture
\| Work with business requirements and design innovative, repeatable solutions
\| Provide guidance for new application features
\| Collaborate with the data staff to create optimal data models for data ingestion and analytics
\| Develop, manage and maintain data models
\| Design implement and maintain database objects (tables, views, indexes, etc.) and database security
\| Maintain performance through tuning, parallelism, optimization, etc.
\| Ensure structure for existing data is effective
\| Design, implement and maintain database objects (tables, views, indexes, etc.) and database security
\| Architect/design and develop large complex ETL jobs
\| Ensure data quality through creation of audit controls, proactive monitoring and data cleansing techniques
\| Maintain the data warehouse performance by optimizing batch processing through parallelization, performance tuning etc.
PROFESSIONAL QUALIFICATIONS
\| Motivated, self\-starter with ability to learn quickly
\| Experienced with SQL, python skills (R is a strong plus)
\| Experience in architecting and engineering innovative data analysis solutions
\| Familiarity with architectural patterns for data\-intensive solutions
\| Expertise in real\-time streaming and migrating batch\-style data processing to streaming and micro\-batch solutions
\| Use of distributed messaging systems to rewrite systems in place
\| Knowledge of the RDBMS core principles; set up, tune, design, as well as newer unstructured data tools
\| Experience developing large scale, complex logical data models (along with physical implementations of the logical models)
\| Familiarity with consulting and traditional application design
\| Experience estimating technical solution builds and contributing to custom proposals
\| Excellent written and verbal communication skills
\| Display solid problem\-solving abilities in the face of ambiguity
\| Must be a hands\-on individual who is comfortable leading by example
\| Experience with Agile Methodology
\| Possess excellent interpersonal and organizational skills
\| Able to manage your own time and work well both independently and as part of a team
TECHNOLOGIES WE USE
Relational/SQL Databases (SQL Server, Postgres, Oracle, MySQL, etc.)
Non\-SQL Databases (MongoDB, Cassandra, ElasticSearch, Neo4j, etc.)
Data Analytics Platforms (Snowflake, Databricks, Apache Spark, Google BigQuery, Azure Synapse, Amazon Redshift, etc.)
Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI)
Familiarity with LLMs, RAG, and Prompt Engineering
Programming Languages (Python, TypeScript, C\#, Java)
AI Productivity Tools (Claude, Claude Code, GitHub Copilot, Cursor, Devin AI, etc.)
EDUCATION \& EXPERIENCE
\| Bachelor’s Degree, preferably in Data Science, Analytics, Computer Science, Engineering or Science / Technology\-based disciplines
\| 10\+ years in data engineering or data architecture
\| Hands\-on experience designing and building scalable data pipelines (ETL/ELT) in a production cloud environment (Azure, GCP, or AWS)
\| Strong data modeling and governance experience: defining source of truth, data quality standards, and golden record definitions
\| Experience designing data layers that feed AI/ML or LLM\-based systems, including structured data transformation for model consumption
\| Proficiency in Python and SQL for pipeline development and data transformation
\| Strong understanding of API design and consumption patterns: connecting data layers to middleware and downstream application consumers
\| Demonstrated ability to lead architecture decisions, document standards, and enforce them across a team
\| Active, daily use of AI coding and productivity tools (e.g. Claude, Claude Code, ChatGPT, GitHub Copilot, or similar)
\| Familiarity with AI observability tooling (e.g. Langfuse or similar)
\| Experience with evaluation frameworks for AI/LLM outputs
\| Experience with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI)
\| Experience with data platforms such as Snowflake or Databricks
COMPENSATION \& BENEFITS
SPR prides itself on helping employees thrive in their work and life. As a result, SPR invests in benefits that include vacation time starting at 3 weeks, 5 days of sick time, 8 paid holidays and 3 floating holidays, maternity, and parental leave, and 401(k) with a match. Employees (and their families) are offered health, dental, \& vision coverage, group life and AD\&D, short/long\-term disability, and flexible spending accounts. Employees receive a $150 reimbursement account for any well\-being\-related expenses and a premium subscription to the Calm app.
SPR is committed to fair and equitable compensation practices. For this position, Data AI Architect, the base salary pay range is $150,000 to $185,000\. In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time.
If this sounds like the kind of challenge you would be up for every day, we would love to hear from you. We are an Equal Opportunity Employer, including disability and veteran.
Salary Context
This $150K-$185K 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 SPR, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($167K) sits 34% below the category median. Disclosed range: $150K to $185K.
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.
SPR AI Hiring
SPR has 1 open AI role right now. They're hiring across AI Architect. Based in Chicago, IL, US. Compensation range: $185K - $185K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.