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Overview: About Us
Working across the globe, V2X builds smart solutions designed to integrate physical and digital infrastructure from base to battlefield. We bring 120 years of successful mission support to improve security, streamline logistics, and enhance readiness. Aligned around a shared purpose, our $4.5B company and 16,000 people work alongside our clients, here and abroad, to tackle their most complex challenges with integrity, respect, responsibility, and professionalism. Responsibilities: What You'll Do
- The AI-Enabled Service Desk Manager leads the daily operations and continuous improvement of the enterprise IT Service Desk, serving as the operational bridge between human support technicians and intelligent automation workflows. This role ensures high-quality technical support while optimizing IT service delivery through agentic AI, self-service capabilities, and platform integrations. Operating within an AI-First IT model, this manager will build toward a self-adapting support environment where automated systems dynamically resolve end-user issues, allowing human technicians to focus on complex escalations and continuous service improvement.
Scope of Responsibility* Manages Service Desk technicians supporting enterprise users across multiple locations while simultaneously overseeing the performance of deployed AI agents and virtual support tools.
- Manages ServiceNow ticket workflows, omnichannel support operations, and quality assurance programs for both human and AI-driven interactions.
- Supports an enterprise environment adhering to Zero Trust architecture principles and integrated identity management systems.
Key Responsibilities* Service Desk Operations & AI Integration + Supervise and guide Service Desk technicians to ensure high-quality support and adherence to service level agreements (SLAs). + Monitor ticket queues and operational dashboards to maintain efficiency, utilizing AIOps and telemetry data to proactively identify and remediate systemic issues before they impact end-users. + Serve as an escalation point for complex technical issues and anomalous AI agent behaviors. + Foster a collaborative, forward-thinking team culture that prioritizes responsiveness and embraces intelligent automation to eliminate manual toil.
- Omnichannel Support & Quality Assurance + Partner with IT teams to improve the omnichannel support experience including phone, chat, service portal, and automated AI support channels. + Conduct regular call monitoring and quality assurance reviews to evaluate technical accuracy and customer service delivery for human technicians. + Establish and monitor human oversight thresholds for agentic AI resolutions, ensuring automated workflows maintain high accuracy and explicitly address user needs. + Analyze IVR metrics, call flow data, and virtual agent interactions to improve routing efficiency, reduce wait times, and enhance the caller experience.
- ServiceNow Platform & Automation Optimization + Partner with ServiceNow administrators and developers to optimize workflows, forms, automation, and enterprise platform integrations. + Implement automation and knowledge-driven support capabilities to autonomously resolve Tier 1 requests, reducing recurring incidents and overall ticket volume. + Ensure proper ticket documentation, categorization, and data integrity to support accurate reporting and continuous machine learning improvements for virtual agents.
- Continuous Service Improvement & Knowledge Management + Identify trends in incidents and service requests to proactively create and update knowledge articles, ensuring content is accurate and optimized for both end-user consumption and AI model grounding. + Implement best practices aligned with ITIL service management frameworks and Zero Trust access controls. + Produce operational reports and dashboards highlighting service desk performance, AI workload resolution rates, and service improvement initiatives for executive leadership.
Qualifications: Minimum Qualifications* Education: + Bachelor's degree in Information Technology (IT), Computer Science or related field or an equivalent combination of education and experience from which comparable knowledge and job skills can be obtained. (One year related experience may be substituted for one year of education, if degree is required)
- Experience: + Minimum 5 years of IT Service Desk, Call Center, or IT Support experience, including leadership or supervisory responsibilities; 8-10 years’ experience preferred.
- Other Requirements: + US Citizenship.
- Required Skills & Competencies: + Hands-on experience with ServiceNow ITSM, including Virtual Agent, workflow automation, and service portal optimization. + Strong understanding of ITIL-based service management practices (ITIL Foundation or V4 certification preferred). + Experience managing or supporting an IVR system and integrating telephony/omnichannel platforms with ticketing systems. + Familiarity with identity and access management platforms (e.g., Okta, Microsoft Entra ID) and Zero Trust device posture requirements. + Demonstrated ability to drive data-driven decision making, process improvement, and cross-team collaboration in a fast-paced, scaling environment.
What We Bring
- At V2X we strive to be market competitive in our total reward offerings.
- The successful candidate’s starting pay will be based on, but not limited to, their job-related skills, experience, qualifications, work location, and market conditions.
- The following salary range is intended to display the value of the company’s base pay compensation and may be modified at the discretion of the company.
- + USD $ 140,000 - 200,000 + Provided salary range minimum and maximum values correspond to variances between regional/geographic locations across the United States.
- Please speak with a recruiter for additional information.
- Employee benefits include the following:
- + Healthcare coverage + Life insurance, AD&D, and disability benefits + Retirement plan + Wellness programs + Paid time off, including holidays. + Learning and Development resources + Employee assistance resources
- Pay and benefits are subject to change at any time and may be modified at the discretion of the company, consistent with the terms of any applicable compensation or benefit plans.
At V2X, we are deeply committed to both equal employment opportunity, including protection for Veterans and individuals with disabilities, and fostering an inclusive and diverse workplace. We ensure all individuals are treated with fairness, respect, and dignity, recognizing the strength that comes from a workforce rich in diverse experiences, perspectives, and skills. This commitment, aligned with our core Vision and Values of Integrity, Respect, and Responsibility, allows us to leverage differences, encourage innovation, and expand our success in the global marketplace, ultimately enabling us to best serve our clients.
Salary Context
This $140K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At V2X, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $140K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
V2X AI Hiring
V2X has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $235K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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