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About This Role
We're ALTEN Technology USA, an engineering company helping clients bring groundbreaking ideas to life—from advancing space exploration and life\-saving medical devices to building autonomous electric vehicles. With 3,000\+ experts across North America, we partner with leading companies in aerospace, medical devices, robotics, automotive, commercial vehicles, EVs, rail, and more.
As part of the global ALTEN Group—57,000\+ engineers in 30 countries—we deliver across the entire product development cycle, from consulting to full project outsourcing.
When you join ALTEN Technology USA, you'll collaborate on some of the world's toughest engineering challenges, supported by mentorship, career growth opportunities, and comprehensive benefits. We take pride in fostering a culture where employees feel valued, supported, and inspired to grow.
Position Summary
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We are seeking a pragmatic, high\-energy Digital AI Accelerator to drive AI literacy, adoption, and practical AI use case discovery at the Airbus Kinston manufacturing site. This role will serve as a catalyst for accelerating the effective use of AI technologies across manufacturing and operational teams by identifying opportunities, educating stakeholders, and translating business challenges into scalable AI solutions.
The ideal candidate combines strong AI literacy across enterprise AI platforms, including Google Gemini, Workspace AI capabilities, and low\-code/no\-code tools, with a solid understanding of manufacturing operations and continuous improvement practices. While deep software engineering expertise is not required, the successful candidate must be able to bridge the gap between operational needs and technical possibilities by defining, prioritizing, and shaping AI\-driven solutions that deliver measurable business value.
Responsibilities
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- Drive AI awareness, literacy, and adoption across manufacturing and operational functions.
- Identify operational inefficiencies, process bottlenecks, and business challenges that may benefit from AI\-enabled solutions.
- Facilitate workshops, demonstrations, and training sessions to increase understanding and practical use of AI tools.
- Partner with manufacturing, engineering, quality, supply chain, and support teams to discover and assess AI opportunities.
- Translate operational requirements into clearly defined AI use cases and solution concepts.
- Evaluate and promote the effective use of enterprise AI platforms, including Google Gemini and Workspace AI capabilities.
- Leverage low\-code/no\-code technologies to rapidly prototype and validate AI\-driven solutions.
- Collaborate with technical teams and stakeholders to scope, prioritize, and implement AI initiatives.
- Monitor AI adoption, user engagement, and business outcomes, providing recommendations for continuous improvement.
- Act as a trusted advisor and change agent, helping teams integrate AI capabilities into day\-to\-day operations.
- Ensure AI initiatives align with business objectives, operational priorities, and governance requirements.
- Maintain awareness of emerging AI technologies, trends, and best practices relevant to manufacturing environments.
Qualifications
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- Bachelor's degree in Engineering, Manufacturing, Information Technology, Business, Data Analytics, or a related field, or equivalent practical experience.
- Demonstrated understanding of artificial intelligence concepts, enterprise AI tools, and generative AI applications.
- Experience using or supporting AI\-enabled platforms such as Google Gemini, Workspace AI tools, or similar enterprise AI technologies.
- Knowledge of low\-code/no\-code platforms and their application to business process improvement.
- Strong ability to identify operational challenges and translate them into actionable technology solutions.
- Experience working within manufacturing, industrial, operations, or production environments.
- Excellent communication, presentation, facilitation, and stakeholder engagement skills.
- Ability to influence and drive organizational change through collaboration and education.
- Strong analytical and problem\-solving skills with a focus on delivering practical business outcomes.
- Comfortable working across technical and non\-technical teams and communicating with all levels of the organization.
- Self\-motivated, energetic, and capable of managing multiple initiatives in a dynamic environment.
- Experience supporting digital transformation, process improvement, continuous improvement, or innovation programs is preferred.
- Familiarity with data analysis, automation tools, and AI governance considerations is a plus.
Salary Range: 90,000 \- 115,000
The actual salary offered is dependent on various factors including, but not limited to, location, the candidate's combination of job\-related knowledge, qualifications, skills, education, training, and experience
All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, age, genetic information, or pregnancy.
*Please beware of job seeker scams and see this* *important notice* *on our careers page for more information about our recruiting process.*
Compliance Notice: Alten USA is a federal contractor subject to the requirements of the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and Executive Order 11246\. We are an Equal Opportunity Employer and consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Drug Screening Requirement: As a federal contractor, Alten USA maintains a drug\-free workplace. All candidates selected for employment will be required to successfully complete a pre\-employment drug screening as a condition of hire.
Salary Context
This $90K-$115K 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 ALTEN, 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 ($102K) sits 52% below the category median. Disclosed range: $90K to $115K.
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.
ALTEN AI Hiring
ALTEN has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kinston, NC, US. Compensation range: $115K - $115K.
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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