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About This Role
About MojoTech:
MojoTech is an innovative technology consulting firm that integrates strategy, design, data, and software engineering to create results-driven, AI-enabled, digital products and experiences.
We partner with our clients to discover, define, design, and build tomorrow’s innovations, empowering every company to unlock future market potential, maximize returns on innovation, and drive transformational change.
Role Overview
We are looking for a Senior Data & AI Architect to lead client engagements across data architecture, AI delivery, and growth.
This role combines deep data engineering expertise with client leadership. You will work directly with client stakeholders to understand their data needs and design the right architecture. You will build the pipelines and AI solutions that turn raw data into actionable insights, and help grow accounts by building trusted relationships and delivering strong outcomes. You should be comfortable leading technical discussions, guiding clients through technology decisions, and staying hands-on in delivery.
Key Responsibilities
Data & AI Architecture
- Design, build, and maintain scalable data pipelines using AWS services (S3, Glue, Lambda, Kinesis, Redshift, Step Functions, etc.) and Python
- Implement and optimize large-scale data processing on modern platforms such as Databricks, Snowflake, and AWS
- Design and deliver end-to-end models and AI agents, from data preprocessing to deployment and monitoring
- Apply Generative AI to data platforms, including LLM integrations and RAG architectures, in production enterprise solutions
- Ensure data quality, reliability, and security across the data lifecycle, supported by CI/CD and DevOps practices
Client Engagement
- Lead discovery efforts, data maturity assessments, and early-stage solution design
- Facilitate architecture discussions with both technical and business audiences
- Translate business needs into clear, executable data and AI roadmaps
Sales & Growth
- Partner with account and sales leads on proposals, estimates, and solution approaches
- Identify follow-on opportunities and clearly articulate the value of proposed solutions
Delivery & Team Leadership
- Lead delivery so work stays aligned with goals, timelines, and budget, and flag risks early
- Mentor engineers and help evolve our practices around data platforms and AI-enabled systems
Qualifications
You should check many of these boxes:
- 7+ years of hands-on data engineering experience, with a strong focus on cloud-native solutions
- Expert-level proficiency with AWS data services. AWS and Databricks certifications are a significant plus.
- Exceptional Python skills for data engineering and automation, plus advanced SQL
- Experience with Databricks or Apache Spark and at least one major cloud ML platform (e.g., AWS SageMaker)
- Proven experience with workflow orchestration tools (e.g., Airflow, AWS Step Functions)
- Solid understanding of data modeling, streaming, and pipeline design patterns
- Experience working in consulting, agency, or client-service environments
- Track record of client and stakeholder satisfaction
- Ability to explain complex technical concepts clearly to non-technical audiences and adapt to diverse client environments
Flexible Working Environment:
We offer a flexible working model, allowing employees to choose between remote work, on-site work in our offices located in Boulder, CO, and Providence, RI, or a hybrid of both. We also welcome candidates based in Phoenix, AZ as we continue to expand our presence in this area.
- This job is remote, but requires you to be located in one of the following states: RI, CO, AZ, CT, FL, MA, MD, ME, MO, NC, NH, NJ, NV, NY, OR, PA, SC, TN or UT. While the role is remote, preference will be given to candidates within commuting distance to our offices in Boulder, CO or Providence, RI.
Benefits:
- Base salary $160,000 - $180,000
- Medical, Dental, FSA
- 401k with up to 4% match
- Trust-based time off
- Catered lunches when in office
- Time for self-directed, non-client work
- Dog-friendly offices
- Remote or in office (offices in Boulder, CO and Providence, RI)
- Paid conference attendance/yearly education stipend
- Generous family leave policy
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Salary Context
This $160K-$180K range is below the median for AI Architect roles in our dataset (median: $175K across 37 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 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At MojoTech, 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 $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($170K) sits 24% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MojoTech AI Hiring
MojoTech has 1 open AI role right now. They're hiring across AI Architect. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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