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About This Role
Who We Are
Accommodations Plus International (API) is a technology and services company focused on driving innovation across the travel and transportation industry. We partner with organizations in the airline, cruise, and rail sectors to deliver solutions that improve layover operations, enhance customer experience, and support long\-term growth.
Our mission is to make layovers simpler and more efficient for crew members—and we bring that to life through deep industry expertise and a practical, results\-driven approach.
Today, API’s platform powers over 18 million crew room nights each year for 100\+ airlines and travel operators worldwide. Our Global Reach ensures that airline crews are rested, transported, and connected so global aviation runs on time.
At API, we’re building a culture rooted in succeeding and thriving together. It’s a place where people are encouraged to take ownership, develop their skills, and contribute to work that matters.
If you’re looking to grow your career in a company that values steady progress, real impact, and long\-term development, we’d like to meet you!
Application Architect — Enterprise \& AI
This is a delivery\-focused architecture role for someone who loves turning complex business problems into working technical solutions.
As an Application Architect, you’ll be the technical lead on product and platform initiatives — taking requirements from product and engineering and designing the systems needed to deliver them. You’ll work closely with delivery teams, product owners, and engineering leads, making the architecture decisions that keep initiatives on track and technically sound.
You’ll be embedded in delivery — shaping how AI\-enabled features, integrations, and services are designed and built responsibly, with security and compliance embedded from the start.
What you’ll own
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Solution design for your initiatives.
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You translate product and business requirements into clear, well\-reasoned technical designs — component diagrams, integration specs, data models, and sequence flows that engineering teams can build from. You work within enterprise reference architecture, applying the right patterns for the problem at hand.
AI \& agent implementation design.
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You design AI\-enabled features and workflows within your initiatives: LLM integrations, agentic flows, retrieval\-augmented generation (RAG), and tool\-use patterns. You make these capabilities concrete, buildable, and production\-ready — informed by the architectural standards set at enterprise level.
Integration \& API design.
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You define how your solution integrates with surrounding services, APIs, event\-driven systems, and external platforms — with clear contracts, resilience patterns, and well\-understood boundaries.
Security \& compliance by design.
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In partnership with AppSec and risk functions, you ensure security controls, PCI/PII requirements, and responsible AI practices are embedded in solution design — not retrofitted during delivery.
Technical governance within delivery.
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You guide and review engineering work throughout the build — attending design sessions, reviewing pull requests where it matters, and helping teams navigate technical decisions without creating bottlenecks.
Estimation \& gate input.
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You produce Gate 0 (ROM) and Gate 1 (detailed) architecture estimates for your initiatives, with clearly documented assumptions, risk flags, and dependency inventories that product and portfolio teams can plan from.
What you’ll deliver
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- Well\-documented solution architectures for each initiative — clear enough that engineering teams can build without constant clarification
- Integration designs with explicit API contracts, event schemas, and data ownership boundaries
- Architecture estimates (Gate 0 and Gate 1\) with assumptions logs and risk registers
- AI feature designs that are production\-ready, governed, and aligned with enterprise AI patterns
- Fewer late\-stage surprises caused by integration gaps, compliance oversights, or unresolved NFRs
Our technology environment
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You’ll be designing across a modern, cloud\-native stack. You don’t need to be an expert in all of it, but you should be able to navigate it confidently and make good architectural decisions within AWS, Java, Angular, Oracle, PostgreSQL, Amazon Kiro, Kafka, SQS, GitHub etc
AI tooling is evolving quickly. We expect our architects to stay close to that evolution — evaluating new capabilities critically and integrating them where they genuinely improve outcomes, not just for novelty.
How we work
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Architecture here is a delivery function, not an ivory tower. We work in agile delivery teams, and solution architects are embedded participants — not external reviewers who parachute in at the start and disappear until go\-live.
Architecture in agile
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You’ll attend sprint ceremonies, backlog refinement sessions, and design reviews as a first\-class team member. Architecture decisions are made iteratively — you’ll produce just enough design upfront to unblock the team, then evolve it as delivery progresses and complexity becomes clearer.
Design reviews and ARB
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Significant architectural decisions are documented in Architecture Decision Records (ADRs) and reviewed by the Principal Architect. You’ll lead the preparation and presentation of your solutions, with the Principal Architect providing governance and pattern alignment rather than sign\-off on every detail.
Cross\-team collaboration.
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Most initiatives touch shared platforms, APIs, or data services owned by other teams. You’ll be expected to negotiate integration contracts, resolve boundary disputes, and align across teams without needing escalation for every decision.
Documentation culture.
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We write things down. Architecture artefacts — diagrams, ADRs, integration specs, assumptions logs — are first\-class outputs of your role, not afterthoughts. Good documentation reduces re\-work, enables team autonomy, and is a mark of craft.
The first 90 days
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Month 1:
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Get across the enterprise reference architecture, approved patterns, and existing system landscape. Meet your product and engineering counterparts. Begin designing the solution for your first assigned initiative.
Month 2:
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Deliver your first application architecture. Run design reviews with delivery teams and iterate based on feedback. Produce a Gate 1 estimate ready for build commitment.
Month 3:
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Embed into delivery rhythm — design sessions, sprint reviews, and integration checkpoints. Begin contributing architectural observations back to the Principal Architect to inform pattern refinement.
What we’re looking for
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- 5–10 years in software engineering, with meaningful experience in solution or technical architecture roles on multi\-team delivery programs
- Solid cloud architecture experience on AWS — services, networking, data, and security patterns in a delivery context
- Hands\-on experience designing and integrating AI\-powered features — including LLMs, RAG patterns, and agentic workflows
- Strong integration design skills — APIs, event\-driven systems, service boundaries, and resilience patterns
- Good working knowledge of backend services (Java ecosystem preferred), data systems, and front\-end integration points
- Experience in regulated environments (PCI/PII) and comfort embedding compliance requirements into design
- Able to produce clear architecture artefacts: component diagrams, sequence diagrams, ADRs, and integration specs
Beyond the credentials, you’re someone who makes things clear and builds trust quickly with delivery teams. You write well — your design docs are readable, your assumptions are explicit, and your trade\-offs are explained. You’re excited about AI’s potential in product but grounded about what that means in practice.
Growth \& career development
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We invest in the growth of our architects. This role sits within a defined architecture career path and comes with structured support to develop in it.
Mentorship from the Principal Architect.
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You’ll have regular 1:1 time with the Principal Architect — not just for design reviews, but for deliberate coaching on architectural breadth, strategic thinking, and stakeholder influence. The Principal Architect is invested in developing application architects who can grow into senior and principal roles over time.
Architecture community of practice.
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We run a fortnightly architecture community that brings application architects, tech leads, and the Principal Architect together to discuss patterns, review emerging technology, and share lessons from delivery. This is a learning environment, not a governance forum.
Career progression.
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For strong performers, the natural path from this role is a Principal Architect, with time and breadth, Enterprise Architect. We’re explicit about what that progression requires and actively support the people who are working toward it.
External development.
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We support ongoing professional development through cloud certifications (AWS Solutions Architect Professional), architecture frameworks (TOGAF), and attendance at relevant industry events and conferences.
Who you’ll work with
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You’ll work closely with product owners, delivery leads, engineering teams, and InfoSec on a day\-to\-day basis. You’ll engage with the Principal Architect on standards alignment, pattern exceptions, and cross\-initiative design issues. You’re the technical voice on solution decisions — with the credibility to challenge scope, surface risk, and unblock delivery.
Compensation:
The good faith compensation for this position is $120,000 \- $130,000 USD, commensurate with experience.
Other Duties
Duties, responsibilities and activities may change at any time according to business needs.
The performance of additional responsibilities if you are designated as a Data Protection Champion (DPC), Senior Information Risk Owner (SIRO) or Information Assurance Accounting Officer (IAAO).
Work Environment
This position operates in a professional office environment. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines.
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to talk or hear. The employee frequently is required to stand, walk; use hands to finger, handle or feel; and reach with hands and arms.
AAP/EEO Statement
Accommodations Plus International is an Equal Opportunity Employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, disability or handicap, sex, marital status, veteran status, sexual orientation, genetic information, arrest record, or any other characteristic protected by applicable federal, state or local laws. Our management team is dedicated to this policy with respect to recruitment, hiring, placement, promotion, transfer, training, compensation, benefits, employee activities and general treatment during employment.
Privacy Statement
API may use the contact information you provide to communicate about this role, including via text message. See our \[Privacy Policy] for details. By clicking "Apply" you agree to Rippling's Terms of Service / User Privacy Notice.
Salary Context
This $120K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $120K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Rippling AI Hiring
Rippling has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, AI Agent Developer. Positions span Melville, NY, US, Columbia, MD, US, Remote, US. Compensation range: $130K - $350K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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