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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 IT Operations Automation \& AIOps Engineer is a hands\-on technical role responsible for designing, building, and operating the automation and intelligent operations frameworks that power the organization’s AI\-first IT environment. Reporting to the VP of Intelligent Automation and IT Operations, this engineer is a core builder of the greenfield IT operational environment — translating manual processes into automated, repeatable, and auditable workflows, and evolving those workflows toward agentic operations where AI systems take autonomous action with appropriate human oversight. This role spans infrastructure provisioning, configuration management, CI/CD pipeline integration, AI\-assisted SDLC tooling, and the orchestration of AI agents across IT operational domains. The destination is not just automation — it is agentic IT operations: a model where AI agents handle routine operational events end\-to\-end while humans focus on strategy, governance, and edge\-case resolution. Responsibilities:* Infrastructure Automation \& Provisioning
+ Design and maintain Infrastructure as Code (IaC) templates using Ansible to provision and manage cloud and on\-premises resources consistently and repeatably.
+ Develop and maintain Terraform playbooks for configuration management, application deployment, patch automation, and compliance enforcement.
+ Build automated provisioning workflows for compute, storage, networking, and end\-user environments.
+ Integrate automation pipelines with ITSM platforms (ServiceNow or equivalent) to enable self\-service IT capabilities.
- CI/CD \& AI\-Native SDLC for IT Operations
+ Implement and maintain CI/CD pipelines for infrastructure code
+ Apply software development best practices — version control, peer review, automated testing — to all infrastructure automation code.
+ Integrate and operationalize AI\-assisted coding tools (GitHub Copilot, Amazon Q, or equivalent) into the IT operations SDLC, enabling AI\-augmented code generation, review, and remediation for automation scripts and runbooks.
+ Develop automated compliance\-as\-code checks to validate infrastructure against security baselines and policy requirements.
- Agentic Operations \& AIOps Platform
+ Operate the AIOps platform, configuring AI\-driven anomaly detection, alert correlation, and automated triage workflows.
+ Build and maintain the agentic workflow orchestration layer: design multi\-step, AI\-agent\-executed operational workflows using orchestration frameworks, enabling IT agents to autonomously handle routine incidents and operational tasks.
+ Define human\-in\-the\-loop thresholds for agentic operations: specify which actions agents may take autonomously, which require human approval, and how agent actions are logged, audited, and reviewed.
+ Integrate automation platforms with monitoring and observability tools to enable closed\-loop remediation — from alert detection through root cause analysis to ticket resolution, without human intervention for defined incident types.
+ Identify and prioritize manual IT operational processes suitable for agentic automation, developing business cases and ROI models for each initiative.
- Cloud \& Hybrid Environment Management
+ Automate provisioning and lifecycle management of workloads across AWS, Azure, and/or GCP environments.
+ Implement FinOps practices including automated tagging, resource scheduling, and rightsizing recommendations to control cloud spend.
+ Maintain automation tooling for hybrid cloud environments, ensuring consistent policies across on\-premises and cloud\-hosted systems.
+ Contribute to disaster recovery automation, including scripted failover and failback procedures with validated RTO/RPO targets.
+ Track and report automation and agentic operations KPIs including tickets automated, MTTR reduction, agent action accuracy, and hours saved per sprint cycle.
- Documentation \& Continuous Improvement
+ Create and maintain comprehensive documentation for all automation frameworks, agentic workflows, runbooks, and operational procedures.
+ Conduct regular reviews of existing automations to identify optimization opportunities, address technical debt, and evaluate candidates for elevation from script\-based automation to agentic orchestration.
+ Mentor junior team members and IT operations staff on automation tooling, AIOps platforms, agentic workflow design, and AI\-assisted coding practices.
+ Track and report automation KPIs including tickets automated, MTTR reduction, agent action success rate, and hours saved per sprint cycle.
Qualifications:
Minimum Qualifications* Educational/Certification Requirements
+ Bachelor’s degree in Information Technology, Computer Science, Systems Engineering, 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
+ A minimum of five (5\) years of experience in IT operations, systems administration, or a related technical discipline.
+ A minimum of three (3\) years of hands\-on experience writing and maintaining automation using Ansible and/or Terraform.
+ Demonstrated experience with scripting languages: Python and/or Bash required; PowerShell a plus.
+ Experience with CI/CD pipelines and version control systems (GitLab, GitHub, or equivalent).
+ Experience with AI\-assisted coding tools (GitHub Copilot, Amazon Q, or equivalent) in a production IT operations context preferred.
+ Familiarity with ITSM platforms such as ServiceNow and integration of automation into ticketing workflows.
+ Experience operating in cloud environments (AWS, Azure, or GCP); multi\-cloud experience a plus.
+ Experience in the government contractor/services industry preferred.
+ U.S. Citizenship required; ability to obtain and maintain a security clearance.
- Other Requirements:
+ Ability to travel to project and customer locations as needed.
- Certifications:
+ Industry certifications preferred: HashiCorp Terraform Associate, Red Hat Certified Engineer (RHCE), AWS/Azure/GCP Associate\-level or equivalent.
+ Ability to obtain and maintain CMMC Level 2 certification.
- Preferred Requirements:
+ Master’s degree or equivalent advanced technical training preferred.
+ Active Secret security clearance desired.
+ Experience with container orchestration (Kubernetes, Docker) and related automation tooling.
+ Familiarity with AI agent governance patterns: human\-in\-the\-loop design, agent audit logging, and permission scoping for autonomous systems.
+ Experience designing or operating agentic IT operations platforms.
- Skills:
+ Strong proficiency in Ansible, Terraform, Python, and Bash scripting.
+ Hands\-on experience with AIOps platforms and closed\-loop remediation design.
+ Familiarity with agentic workflow orchestration frameworks and human\-in\-the\-loop design patterns.
+ Working knowledge of AI\-assisted SDLC tools and their application to IT operations code.
+ Working knowledge of networking fundamentals sufficient to automate network configuration tasks.
+ Familiarity with security baselines, CIS benchmarks, and compliance\-as\-code practices.
+ Strong problem\-solving skills and a bias toward automation\-first and agentic\-operations thinking.
+ Clear written and verbal communication skills, including the ability to document technical processes for non\-technical audiences.
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 $ 145,000 \-235,000
- 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 $145K-$235K range is above the median 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 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 $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 ($190K) sits 12% below the category median. Disclosed range: $145K to $235K.
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.
V2X AI Hiring
V2X has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $235K - $235K.
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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