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About This Role
About the Opportunity
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Direct Travel is building artificial intelligence into how we operate and what we deliver to our clients. Our objective is what we call the perfect trip — a travel experience where intelligent systems anticipate needs and remove friction at every step. The Director of AI is instrumental to delivering this strategic vision and roadmap.
The central mandate is agentic. Leveraging our data foundation, you will enable intelligent agents to drive business processes end to end — establishing the AI platforms and frameworks the organization builds on, driving the engineering of the harnesses those agents operate within, and measuring rigorously whether they work.
This is a hands\-on leadership role. You will own an AI budget, a global team of full\-time and contract engineers, and a vendor portfolio, while remaining active in architecture and design decisions. Technical credibility with the team you lead is essential.
What You Will Own
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Agentic AI \& Intelligent Process Automation
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- Define and deliver the agentic AI strategy — intelligent agents operating on our consolidated data to drive business processes end to end.
- Drive the engineering of the harnesses these agents run inside — tool access, context management, control flow, error recovery, and guardrails — and set integration standards including the Model Context Protocol (MCP) and Agent2Agent (A2A).
- Build the evaluation discipline that proves an agent works: automated evaluations, regression testing, and clear criteria for promoting from pilot to production.
- Define where humans stay in the loop, particularly where decisions carry financial and service consequences.
AI Platforms \& Frameworks
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- Establish the AI platforms, frameworks, and reference architectures the organization builds on, so capability compounds rather than being rebuilt project by project.
- Own model strategy: which providers, which models for which workloads, and when a smaller or self\-hosted model beats a frontier one.
- Own AI unit economics — inference cost, cost per workflow, and the decisions that keep spend proportionate to value.
AI in Products \& Client\-Facing Services
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- Build AI into the services and products our clients use, in direct pursuit of the perfect trip.
- Own the AI capabilities embedded in travel operations — vendor platforms where they fit, built in\-house where they do not.
Internal Productivity \& AI Adoption
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- Raise organizational output through AI — internal tools, chatbots, copilots, and automation people actually adopt — and prioritize what is worth funding.
- Lead AI literacy across the organization, building genuine capability in the teams using these tools daily.
- Drive adoption against the ordinary friction of change.
- Continue establishing an AI\-driven engineering culture, where building with AI is normal practice and intentional.
Governance, Risk \& Compliance
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- Ensure AI complies with enterprise governance, cybersecurity, data privacy, and regulatory standards, partnering with security, legal, and privacy so it is built into how we ship.
- Build toward ISO/IEC 42001 alignment and EU AI Act readiness, including risk classification of AI use cases and the supporting documentation.
- Own AI\-specific security posture: prompt injection, exfiltration through agent tool access, over\-permissioned agents, and the review process that catches them.
Team, Vendors \& Budget
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- Lead and grow a global team of full\-time and contract engineers, and own the AI budget across headcount, vendor platforms, infrastructure, and model spend.
- Own build\-versus\-buy judgment. We run a select portfolio of vendor AI platforms; you drive what stays, what gets replaced, and what we build ourselves with engineering leadership.
- Manage vendor relationships as a core function: evaluation, negotiation, performance management, and knowing when to walk away.
Required Qualifications
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If you meet most of what follows and the mission fits, we would rather hear from you than not.
Leadership \& Experience
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- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, or a related technical discipline required.
- 10\+ years in software engineering leadership, including 5\+ leading AI transformation at organizational scale — not isolated pilots.
- Experience leading global teams that mix full\-time employees and contract engineers.
- A demonstrable track record of using AI to scale organizational output.
- Experience establishing an AI\-driven engineering culture, and owning a budget including vendor spend and build\-versus\-buy decisions.
Engineering \& Technical Depth
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You will be in architecture and design reviews. We want depth, not familiarity.
### Hands\-On Engineering:
- Strong engineering fundamentals and current hands\-on credibility — you contribute to architecture, design, and implementation rather than only reviewing them.
- Solid command of the software development lifecycle, source control, CI/CD, and production operations.
### Applied AI \& Agentic Systems:
- Have personally built and shipped production AI systems, including agentic ones, and can account for how they were architected, integrated, and evaluated.
- Practical experience implementing retrieval\-augmented generation (RAG) in production, including its failure modes.
- Fluency in agent integration — MCP, A2A, or equivalents you have built yourself — and in evaluation practice: measuring quality, catching regressions, and knowing when something is ready.
### The Current AI Horizon:
- Current understanding of the model landscape — frontier and open\-weight models, their tradeoffs, and how fast it changes.
- Fluency across major cloud providers and their AI services, and command of AI unit economics: inference cost, model routing, and self\-hosting tradeoffs.
AI Governance \& Risk
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- Working understanding of the AI regulatory landscape, including ISO/IEC 42001 and the EU AI Act, and what they require in practice.
- Experience delivering AI inside enterprise governance, cybersecurity, and data privacy constraints, and understanding of AI\-specific security risks.
Communication \& Mindset
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- Able to explain AI capability and limitation honestly to executives, clients, and engineers.
- Comfortable making principled decisions in a field that outpaces written requirements.
- Willing to say when AI is the wrong tool.
Preferred Qualifications
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- Master's degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field preferred. Equivalent combination of education and directly relevant experience will be considered.
- Hands\-on experience building ML models from the ground up — training, tuning, and deployment, not API consumption alone.
- A data science background sufficient to interrogate model behavior and evaluation results.
- Experience in travel, hospitality, or logistics, particularly at global or multi\-brand scale.
- Experience with workflow automation platforms, particularly n8n.
- Experience operating AI capabilities in multi\-tenant environments serving external clients.
- Experience taking an organization through ISO/IEC 42001 certification.
Benefits Summary
In addition to Medical, Dental, and Vision benefits Direct Travel offers a Total Rewards Package which includes Retirement Plans, Wellness, Rewards and Recognition, Sustainability, DE\&I initiatives, and Mental Health Support.
About Direct Travel
Direct Travel is one of the fastest growing Travel Management Companies (TMC) in the world. Leveraging the expertise of its people and innovative technology solutions, Direct Travel enables clients to derive the greatest value from their travel program through superior service, progressive technologies, and significant cost savings. Direct Travel has offices globally and is currently ranked among the top providers on Travel Weekly's Power List. For more information, visit www.dt.com.
*Direct Travel is an equal opportunity employer*
*\#LI\-Remote*
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 Direct Travel, 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. Director-level AI roles across all categories have a median of $274,554.
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.
Direct Travel AI Hiring
Direct Travel has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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