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About This Role
### Redefine the future of customer experiences. One conversation at a time.
At Nextiva, we’re reimagining how businesses connect, bringing together customer experience and team collaboration on a single, conversation centric platform. Powered by AI, driven by human innovation.
Our culture is forward thinking, customer obsessed and built on the belief that meaningful connections drive better business outcomes. Whether it’s through our signature Amazing Service®, the technology we create, or the experiences we cultivate, connection is at the core of who we are.
If you’re ready to collaborate with incredible people, make an impact, and help businesses everywhere deliver truly amazing experiences, this is where you belong.
Location: This is an onsite role based at Nextiva’s Scottsdale headquarters (9451 E. Via de Ventura, Scottsdale, AZ 85256\). Working together onsite strengthens how we operate, enabling faster decisions, clearer communication, and stronger execution, so you can make a greater impact and move work forward with speed and clarity.
In\-Office Expectation: This role is expected to work onsite five days per week, supporting a highly collaborative, in\-person team environment.
As an AI Engineer on Nextiva’s AI Operations team within Corporate Strategy, you’ll build, ship, and maintain AI\-powered tools that change how the business operates. This is a hands\-on role focused on turning ideas into working systems, from prototypes to production.
You’ll work across the full stack, building internal AI tools and integration pipelines that connect directly to business workflows — and redesigning those workflows around what AI makes possible. You’ll help shape Nextiva’s internal AI platform and drive measurable improvements in how teams operate.
Top 3 Outcomes (first 12 months)
- Build and deploy AI\-powered applications that accelerate workflows and improve internal velocity across departments
- Re\-engineer core business processes — not by layering AI onto existing workflows, but by restructuring how work gets done to improve efficiency and operating leverage
- Serve as a technical leader within AI Operations, mentoring interns, guiding architectural decisions, and leading technical interviews for team growth
Key Responsibilities
- Lead end\-to\-end design, development, and deployment of internal AI\-powered applications (tools, workflows, agents).
- Identify, scope, and re\-engineer business processes where AI can remove manual effort and improve outcomes.
- Architect and build integration frameworks and Python\-based orchestration that connect AI tools to business systems.
- Work with IT, Security, and DevOps to ensure safe and scalable deployment practices.
- Own delivery within the tiered build model (no\-code \<1 day, low\-code \<5 days, custom \<4 weeks) and maintain development velocity targets.
- Define technical standards, documentation, and best practices for reliability, quality, and observability.
- Contribute to internal enablement programs to drive company\-wide AI adoption.
Requirements
Must\-have
- Proven ability to build, ship, and scale production AI\-powered applications
- Strong Python skills and experience with frameworks like FastAPI
- Experience mapping and re\-engineering business processes, with a focus on automation and measurable efficiency gains
- Demonstrated leadership in mentoring or technical hiring.
- Excellent communication and documentation skills; ability to collaborate with technical and non\-technical stakeholders.
- Security\-first mindset and experience managing data access/governance.
Preferred
- Experience in enterprise SaaS or high\-growth tech environments.
- Experience with internal tool development and rapid prototyping.
- Understanding of Salesforce, productivity workflows, or revenue operations.
Tools \& Tech Stack
- Languages \& Frameworks: Python (FastAPI), TypeScript (React)
- Infrastructure: Docker, Gunicorn, Nginx, GitHub Actions, GCP, CI/CD, internal API frameworks
Nextiva DNA (Core Competencies)
Nextiva’s most successful team members share common traits and behaviors:
- Drives Results: Action\-oriented problem solvers who quickly bring clarity and simplicity to ambiguity, challenge the status quo, and lead meaningful change; celebrating wins to fuel momentum. They act swiftly and pragmatically, learning and improving as they go.
- Critical Thinker: Data\-driven, forward\-thinking individuals who identify key drivers, anticipate risks, and deliver clear recommendations. They confidently leverage AI and automation to reduce friction, improve decision\-making, and focus on higher\-value work.
- Right Attitude: Collaborative, competitive, and resilient team players who jump in to solve tough problems, learn from setbacks, and foster a culture of service, respect, and care for customers and teammates.
#### Total Rewards
Our Total Rewards offerings are designed to allow Nexties to take care of themselves and their families so they can be their best, in and out of the office.
Our compensation packages are tailored to each role and candidate's qualifications. We consider a wide range of factors, including skills, experience, training, and certifications, when determining compensation. We aim to offer competitive salaries or wages that reflect the value you bring to our team. Depending on the position, compensation may include base salary and/or hourly wages, incentives, or bonuses.
- Health: Multiple health plan options to suit your needs, including medical, dental, vision, and telemedicine coverage
- Insurance: Life, disability, and supplemental indemnity plans
- + ️ Work\-Life Balance: Flexible Time Off for salaried employees, PTO for hourly employees, Paid Sick Time, Paid Parental Bonding Leave, and holiday pay
- Financial Security: 401(k) with company match, Health Savings Accounts with company contributions, Dependent Care FSA
- Wellness: Employee Assistance Program (EAP) and comprehensive wellness initiatives
- Growth: Access to ongoing learning and development opportunities and career advancement
At Nextiva, we're committed to supporting our employees' health, well\-being, and professional growth. Join us and build a rewarding career!
Nextiva is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Nextiva participates in the E\-Verify Program where and as required by law. .
\#LI\-MS1 \#LI\-Onsite
Founded in 2008, Nextiva has grown into a global leader trusted by over 100,000 businesses and 1M\+ users worldwide. Headquartered in Scottsdale, Arizona, and with teams across the globe, we're the future of customer experience and team collaboration through our AI\-powered, conversation\-centric platform.
Want to see what life at Nextiva is all about?
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nextiva, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Nextiva AI Hiring
Nextiva has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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).
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 3,708 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
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