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About This Role
As an AI Engineer, you will be the technical engine behind every AI implementation the company runs, setting up the models, building the safety and reliability infrastructure, and establishing the engineering standards that every future AI project will inherit.
This is a greenfield role with high ownership. You will be designing and building the foundational AI platform that Hotwire's business units depend on. You'll partner closely with the Director of AI Implementation and AI Champions embedded in each business unit, translating validated workflow proposals into production\-grade AI solutions.
Duties / Responsibilities:
- Design and build the core AI platform that connects Hotwire's business applications, data sources, and AI models into reliable, production\-grade pipelines
- Own the model deployment layer, configure, version, and maintain LLM endpoints across Azure OpenAI and/or AWS Bedrock with environment isolation (dev / staging / prod)
- Implement a model abstraction layer (e.g., LiteLLM) to ensure portability across model providers and avoid hard vendor lock\-in
- Build and maintain an internal AI SDK / shared libraries so that future engineers and CoE projects can bootstrap quickly without reinventing plumbing
- Own infrastructure\-as\-code and CI/CD pipelines for AI services Other duties as required or assigned.
- Actively participate in Steering Committee reviews, translating technical risk and feasibility into language business leaders understand
- Build and enforce input/output security controls for every AI\-facing endpoint:
- PII detection and redaction before data reaches external model APIs
- Prompt injection detection, pattern\-based and embedding\-based classifiers
- Content policy filtering and output moderation for customer\-facing AI surfaces
- Role\-based access control to AI capabilities across business units
- Partner with IT Security and Compliance to ensure every AI deployment meets Hotwire's data residency, encryption, and access audit requirements
- Maintain a centralized secrets management approach for API keys, model credentials, and third\-party integration tokens
- Implement an LLM evaluation framework that every CoE project must pass before production promotion
- LLM\-as\-judge pipelines for automated output quality scoring
- Regression test suits that protect against model drift when providers update underlying models
- Semantic similarity and coherence metrics for RAG\-based applications
- Golden dataset management and versioning for reproducible evals
- Own the eval harness integration into CI/CD, no model change ships without passing eval thresholds
- Track and report quality metrics to the Director and Steering Committee as part of the AI implementation lifecycle
- Build operational safety infrastructure around AI services:
- Rate limiting and token\-budget enforcement per business unit and use case
- Circuit breakers to prevent downstream cascades when model APIs degrade
- Iteration caps and wall\-clock timeouts on agentic workflows
- Async queue management and retry logic for high\-volume pipelines
- Configure private endpoints and VNet integration for model APIs to keep data off public internet paths
- Implement cost allocation and spend controls so that per\-department AI usage is visible and accountable
- Set up comprehensive tracing and monitoring across all AI services using tools such as LangSmith, LangFuse, or equivalent
- Build dashboards that surface latency, error rates, token consumption, quality scores, and cost per workflow, visible to both engineering and business stakeholders
- Establish alerting thresholds and on\-call runbooks for AI service degradation
- Maintain audit logs of all model inputs and outputs for compliance review
- Serve as the technical reviewer for AI workflow proposals coming from business unit AI Champions before they reach the Steering Committee
- Write engineering standards, integration patterns, and runbooks that AI Champions and future engineers can follow
- Contribute to vendor evaluations, help assess new AI tooling, model releases, and platform options
- Other duties as required or assigned by supervisor.
Minimum Qualifications:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
- 2\-4 years building and operating production LLM applications, not prototypes, not demos, production systems with real users and real SLAs
- 4 years of software engineering experience with a strong bias toward system design and production\-grade architecture
- Expert\-level Python, you write clean, tested, maintainable Python, not just scripts
- Deep understanding of API design, microservices patterns, async programming, and distributed system fundamentals
- Hands\-on experience with CI/CD pipelines, containerization (Docker), and cloud\-native deployment
- Strong debugging instincts, you can trace a failure from a user\-facing symptom down to a model API edge case
- Experience deploying and managing LLMs on enterprise cloud platforms: Azure OpenAI Service or AWS Bedrock
Benefits:
We truly appreciate and value all our employees and show our appreciation by offering a wide range of benefits, including:
- Comprehensive Healthcare/Dental/Vision Plans
- 401K Retirement Plan with Company Match
- Paid Vacation, Sick Time, and Additional Holidays (including your Birthday!)
- Paid Volunteer Time
- Paid Parental Leave
- Hotwire Service Discounts – for employees who live on a property serviced by Hotwire. Discounted service offerings are provided for high\-speed internet, video service, phone, and security service
- Employee Referral Bonuses
- Exclusive Entertainment Discounts/Perks
Hotwire provides equal employment opportunities to all employees and applicants for employment and prohibits 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.
\#LI\-MC1
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Hotwire 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 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Hotwire Communications AI Hiring
Hotwire Communications has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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