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About This Role
Position Summary
The AI Business Analyst turns Bluum's business priorities into work that the development team and outside partners can build, and manages those partners through delivery. Sitting between sales, revenue operations, marketing, enablement and Information Systems, you will document how work is done today, design how it should work with AI-enabled tools, and write the requirements that developers and vendors build from. You will own two programs from the start: the rollout of Aviso, Bluum's sales automation platform, across roughly 130 sellers and leaders; and the Zaigo Labs Control Desk program, a custom build in Bluum's Azure environment that automates order status, AV proposals and quoting. Working alongside the Developer - Integrations & AI, you will facilitate the Claude User Group, own the intake and routing of the process improvements it surfaces, and guide Bluum's Claude setup so that context, prompts and connectors are shared rather than rebuilt by each user.
Responsibilities
- Own the Aviso platform configuration, use-case backlog and release plan, from the pilot through the launch at Sales Kickoff and the phases that follow.
- Serve as Bluum's business owner for the Zaigo Labs Control Desk program: lead discovery, map current-state workflows and exceptions, set pilot boundaries and decision gates, and accept delivered software against agreed criteria.
- Document current-state processes through interviews, shadowing and screen recordings, and design future-state workflows across Aviso, the Control Desk and Bluum's internal AI services.
- Write clear requirements, user stories, data mappings and acceptance criteria for the Developer - Integrations & AI, the Information Systems team, Aviso and Zaigo.
- Serve as the business point of contact for Aviso's customer success team and Zaigo's delivery team, coordinating scope, timelines, testing and handoff into Bluum's Run organization.
- Map NetSuite objects, fields and workflows (opportunities, quotes, activities, accounts, sales orders) to platform configurations and integration designs, and validate data flows with Information Systems.
- Plan and run user acceptance testing with sellers, sales directors, revenue operations, order desk and engineering; capture defects and drive them to resolution.
- Build training materials and run enablement sessions with the Sales Coach; support onboarding of new sellers and order desk staff to the new tools.
- Define and report adoption and outcome measures with Revenue Operations, including the monthly scorecard to the Senior Leadership Team and quarterly board reporting, for both programs.
- Run the intake and prioritization process for AI and automation requests across departments, scoring them for value, effort and risk, and routing them to buy, build or configure.
- Facilitate the Claude User Group: run the weekly share of wins, maintain the shared prompt and skill library, and route each process improvement it surfaces to an existing system, Aviso, the Zaigo Control Desk or an individual Claude workflow.
- Guide Bluum's Claude infrastructure with Information Systems: a shared context hub per project, a prompt library on a Bluum MCP server, and the connectors that give Claude governed access to business data.
- Identify and document new use cases (engineering handoffs, RFP intake and scoring, AV opportunity prioritization, account planning signals) and scope them with vendors and developers.
- Maintain process and configuration documentation, follow change control practices, and perform other analysis and project-related tasks as assigned.
Education & Skill Requirements
- Bachelor's degree in Business, Information Systems, or a related field, or equivalent practical experience.
- Three or more years as a business analyst, sales operations analyst, revenue operations analyst or implementation consultant.
- Hands-on experience administering or implementing Aviso, and a track record of transforming a business process using Aviso or a comparable revenue platform (for example Outreach, Gong, Clari or Salesloft).
- Experience managing an outside development partner or systems integrator through discovery, build, testing and handoff.
- Experience working with a CRM or ERP used as the system of record; Oracle NetSuite experience is a strong plus.
- Ability to write requirements, user stories and acceptance criteria that developers can build from without re-interpretation, and to read data mappings and API documentation.
- Working knowledge of AI-enabled tools (LLM assistants, agents, prompt design) and their practical limits in business workflows.
- Daily hands-on use of Claude or a comparable AI assistant, including building reusable prompts, skills or connectors, and coaching others to do the same.
- Experience running user acceptance testing, training and change management with field sales and operations teams.
- Strong facilitation and communication skills across sales, operations, marketing and technical audiences; comfortable with spreadsheets, basic SQL or reporting tools, and defining metrics.
- K12 education, public-sector sales or technology reseller experience is a plus.
Core Values
- Start with Empathy - We lead with curiosity, compassion, and respect. We listen to each other and to our customers, value different perspectives, and work to understand before being understood. That’s how we build trust and better solutions.
- Own it Together - We are accountable. We do what we say we will do. We hold ourselves, and each other, accountable for results. We follow through on commitments and take responsibility for our impact on our team, our customers, and our mission.
- Do What’s Right - We act with integrity, even when it’s hard. We’re honest, ethical, and guided by what’s best for our customers, our team, our partners, and the students we serve – always.
- Are Always Learning - Like the educators and students we serve, we believe in lifelong learning. We embrace change, seek feedback and stay curious—because every challenge is a chance to grow stronger, smarter, and more impactful.
- Win as a Team - We're in this together. We put the mission and the team first. We make decisions that benefit our customers and our company. We succeed by aligning around shared goals and celebrating collective wins over individual gain.
Training
- It will be necessary to participate in upgrading courses from time to time to keep abreast of changing policy, technology, and growth.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Bluum, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Bluum AI Hiring
Bluum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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