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About This Role
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 Manager, Monitoring & Workload Health is a technical leadership role responsible for the continuous observability, performance, and resilience of the enterprise IT ecosystem. Reporting to the Director of Adaptive Infrastructure Network & Security, this leader will drive the transition from reactive infrastructure monitoring to proactive, self-healing operations. By leveraging agentic AI alongside enterprise monitoring platforms such as Microsoft System Center Operations Manager (SCOM) and SolarWinds Orion, the Manager will optimize the health of cloud workloads, on-premises infrastructure, network devices, and SaaS applications. This role is critical to establishing a resilient AIOps environment where predictive failure analysis and automated remediations eliminate operational downtime and reduce manual toil for the engineering teams.
Key Responsibilities:* AIOps Strategy & Advanced Monitoring
- + Design, deploy, and manage enterprise monitoring solutions, specifically focusing on optimizing SolarWinds Orion and Microsoft SCOM across a hybrid IT environment. + Integrate network telemetry, system logs, and application performance data into a centralized AIOps platform to support intelligent operations and anomaly detection. + Define and enforce monitoring baselines, thresholds, and intelligent alerting rules that prioritize business-critical SaaS applications and AI workloads. + Develop observability strategies that provide end-to-end visibility into complex infrastructure paths, including Zero Trust network segments and multi-cloud environments.
- Predictive Failure & Agentic Remediation
- + Implement agentic AI workflows to identify degradation patterns and execute predictive failure analysis before system outages occur. + Build and maintain automated remediation runbooks utilizing tools like Ansible, Terraform, and Python to allow AI agents to execute self-healing actions on infrastructure and network devices. + Establish strict governance and human-in-the-loop oversight thresholds for agent-driven automated remediations, ensuring safe and compliant execution within the production environment. + Lead post-incident reviews (RCA) leveraging AI-generated timelines and telemetry data to continuously tune predictive algorithms and prevent recurring issues.
- Workload Health & Performance Optimization
- + Monitor AI model inference traffic, LLM API calls, and agent-to-agent communication, ensuring the underlying infrastructure meets required Quality of Service (QoS) and latency SLAs. + Collaborate with infrastructure engineering and cloud teams to right-size compute workloads based on automated capacity planning and performance trend analysis. + Develop and deliver performance-based KPIs to IT leadership, highlighting system uptime, mean time to remediate (MTTR), and the effectiveness of automated resolution rates.
- Team Leadership & Continuous Improvement
- + Manage the day-to-day tasking, performance, and effectiveness of a team of monitoring engineers and AIOps developers. + Foster a culture of automation-first thinking, guiding the team to codify operational policies and build "Compliance-as-Code" and "Self-healing" network capabilities. + Manage enterprise vendor and supplier agreements related to monitoring platforms, ensuring technical debt is minimized and platforms are optimized for value.
Qualifications: Minimum Requirements* Education: + Bachelor’s degree in Information Technology, Computer Science, Systems Engineering, or a 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: + 5-10 years of experience designing and operating complex enterprise monitoring, infrastructure, or reliability engineering solutions.
- Leadership: + Proven experience as a manager or team leader overseeing technical personnel and large-scale automation projects.
- Other Requirements: + U.S. Citizenship
- Required Skills & Competencies + Deep technical expertise in administering and scaling Microsoft SCOM and SolarWinds Orion in enterprise environments. + Strong proficiency in network automation, Infrastructure-as-Code (IaC), and scripting languages (PowerShell, Python, YAML, JSON) to drive automated remediation workflows. + Experience integrating AIOps tools and agentic AI models with ITSM platforms (e.g., ServiceNow) for closed-loop incident resolution. + Understanding of modern cloud architectures (Azure, AWS), virtualization (VMware, Hyper-V), and Zero Trust networking principles. + Exceptional collaboration skills to partner with service desk, security, and application teams to build a cohesive, automated operational fabric.
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 + Retirement plan + Life insurance, AD&D, and disability benefits + 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 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 $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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