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General Purpose:
The Business Analyst of AI \& Automation is a key member of the technology and digital transformation team, serving as a strategic partner to business stakeholders, with responsibilities to identify, define, and prioritize opportunities for automation, AI\-driven decisioning, and intelligent workflows across the enterprise.
This role works at the intersection of business strategy, process optimization, data, and emerging technologies. The Business Analyst will translate business problems and operational inefficiencies into clear functional requirements and solution designs to be implemented across a variety of platforms, including automation tools (RPA, workflow orchestration), AI/ML solutions, copilots/assistants, analytics platforms, and integrated enterprise systems.
Success in this role requires strong business acumen, technical fluency (not platform\-specific), excellent communication skills, and the ability to influence across functions. The ideal candidate can translate ambiguity into recommended courses of action, solution frameworks, and implementation roadmaps, guide stakeholders toward pragmatic automation and AI use cases, and ensure solutions deliver measurable business value while remaining scalable, governed, and ethically sound.
This role operates with a high degree of autonomy, regularly exercising discretion and independent judgment on significant matters from evaluating competing priorities and assessing risk to making recommendations that shape business operations, process design, and technology strategy.
Direct Reports: No
Essential Duties and Responsibilities:
Strategic Partnership \& Discovery
- Serve as a trusted partner to business leaders to identify, recommend, evaluate, and prioritize automation and AI opportunities aligned with strategic objectives, operational efficiency, customer experience, and revenue growth.
- Conduct discovery workshops to understand current\-state processes, pain points, decision drivers, and success metrics, and convert those understandings into actionable solutions.
Requirements \& Solution Design
- Elicit, analyze, and document business, functional, non\-functional, and data requirements for automation and AI initiatives.
- Translate business needs into clear solution specifications for:
+ Process automation (RPA, workflow, orchestration)
+ AI\-assisted workflows and decision support
+ Predictive, generative, or rules\-based capabilities
+ Data ingestion, enrichment, and outputs
- Develop epics, features, user stories, and acceptance criteria using agile methodologies.
- Provide strategic input to architects, engineers, data scientists, and vendors to ensure solutions meet business intent without unnecessary complexity.
Process Optimization \& Change Enablement
- Assess and redesign business processes to ensure they are automation\-ready before technology implementation.
- Identify change management needs, risks, and dependencies to minimize disruption to business operations.
- Support stakeholders in understanding how AI and automation will change roles, workflows, and decision\-making.
Implementation Oversight
- Oversee and provide guidance across multiple concurrent initiatives using agile, hybrid, or iterative delivery models.
- Direct and assess solution configuration, validation, and testing in collaboration with delivery teams.
- Lead User Acceptance Testing (UAT), ensuring outcomes align with documented business objectives.
- Assist with go\-live readiness, training materials, and adoption plans.
Governance, Risk, \& Value Realization
- Collaborate with security, legal, compliance, and data teams to ensure AI and automation solutions comply with internal policies and external regulations.
- Ensure responsible AI practices, including transparency, bias awareness, and explainability where applicable.
- Define, establish, and track KPIs to measure automation and AI value realization post\-implementation.
- Continuously identify opportunities to optimize or expand deployed solutions.
Other Responsibilities:
- Stay current with trends, best practices, and emerging capabilities in AI, automation, and intelligent workflow technologies, and make recommendations for adoption based on evaluation of same.
- Evaluate new tools and approaches objectively, independent of vendor or platform bias.
- Proactively identify opportunities to improve existing automations and AI\-driven processes
Required Qualifications:
- BA/BS in Information Systems, Computer Science, Business, Engineering, or equivalent experience.
- Proven experience as a Business Analyst supporting automation, AI, data\-driven, or complex enterprise technology initiatives.
- Strong understanding of business processes, operating models, and how technology enables measurable outcomes.
- Demonstrated ability to balance strategic thinking with practical, execution\-focused delivery.
- Comfortable working in environments with evolving requirements and emerging technologies.
- Highly self\-motivated, adaptable, and capable of managing multiple initiatives simultaneously without sacrificing quality.
- Exceptional written and verbal communication skills across technical and non\-technical audiences.
- Ability to advise and translate complex automation or AI concepts into actionable business recommendations at an executive level.
- Skilled at facilitating alignment among diverse stakeholders with competing priorities.
- Strong collaboration skills with product managers, architects, engineers, data teams, and external partners.
- Ability to decompose complex topics into consumable components that drive clarity and momentum.
Other Qualifications:
- Experience with one or more of the following is strongly preferred:
+ Process automation or workflow platforms
+ AI/ML or analytics initiatives
+ Data modeling or data governance concepts
+ Agile product delivery environments
- Familiarity with responsible AI principles and enterprise governance models is a plus.
- Demonstrated ability to exercise sound judgment, balance competing priorities, and guide strategic solutions in complex, ambiguous situations.
TPx is an Equal Opportunity / Affirmative Action employer. Qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy, childbirth, breast\-feeding and related medical conditions), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability (including medical condition), military or veteran status, political preference, marital status, citizenship, genetic information or other status protected by law or regulation.
We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please let us know during the application process.
\#LI\-Remote
Req: \#26\-0072
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TPx Communications, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TPx Communications AI Hiring
TPx Communications 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 $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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