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What success looks like in this role:
The rise of AI has caused a fundamental shift in the workplace. The organizations that lead this transition won’t win by doing the old job faster, they will win by embracing the change and rethinking the job entirely.
Often, this work is split in two: giving people access to and training on the tools, and separately trying to manage the change.
You'll support both halves of the problem: the curriculum that builds people's ability to know when and how to work with AI, and the operating model that turns that judgment into an adopted, governed, and measured practice across the organization.
You’ll need to think both strategically and tactically. Some days present a high-level, multi-year strategy question; other days, it's working with one team on one workflow to figure out together what "working differently" means today. Both halves are held to the same bar: driving business impact and improving the daily lives of our employees.
Key responsibilities:
- Define and deploy, globally. Map how the work is done today, end-to-end, pinpointing opportunities for AI integration across organizations (such as internal operations, client delivery, pre-sales, and product).
- Collaboratively define adoption and governance operating models. Help design the model, sponsorship, measurement, feedback loops, guardrails, as part of a broader team effort, and help drive how it runs while minimizing resistance and disruption.
- Drive AI fluency as a durable capability. Oversee the creation of scalable learning experiences that give people real judgment about delegation, discernment, and diligence with AI, model-agnostic.
- Support practical upskilling. Oversee the creation and/or curation of resources that ensure that people have the functional ability to leverage AI in the flow of work.
- Contribute to Unisys presales success. Shape custom Organizational Change Management (OCM) and training strategies that win client pursuits and ensure long-term adoption.
- Think strategically in terms of what the organization’s work might look like in five years, and also in terms of the specific exercise, workflow, or conversation that needs to happen now to move toward it.
- Measure behavior and prove it’s worth it. Define what changed practice looks like for each audience and tie adoption back to real business outcomes and to the actual work lives of the people doing the work.
- Stay ahead of the curve. Track how AI capabilities, tools, and norms are shifting over time, and revise curriculum and guardrails before they go stale.
- Provide change management support across various initiatives and projects and for broad segments of the organization.
- Drive processes to ensure key stakeholders understand the change implications associated with outcomes and deliverables.
- Identify and work with stakeholders to develop and implement strategies that help them thrive in continuous change.
- Ensure change enablement strategies and actions are incorporated into technology implementations and steady state operations.
- Play a key role helping to define and measure success metrics and monitor progress as well as business outcomes.
You will be successful in this role if you have:
BA/BS degree and 6-8 years’ relevant experience OR equivalent combination of education and experience
Prosci Certified Change Practitioner, or equivalent OCM certification preferred
You may be a good fit if you:
- Have originated a team or function that didn't exist before.
- Have successfully deployed AI-forward initiates that drive transformation across an enterprise organization.
- Have built learning or enablement programs from scratch, at organizational scale that successfully changed behavior.
- Have a proven track record of leading cross-functional teams across a variety of functions and disciplines.
- Operate from a data-driven mindset, using analytical rigor to define success and translate outcome data into clear actionable insights.
- Are comfortable with ambiguity, understanding that real impact often involves pivoting based on the changing AI landscape and business needs.
Strong candidates may also have:
- A strong background in instructional design, adult learning theory, change management, or organizational psychology.
- A proven track record of looking beyond conventional playbooks to uncover creative solutions to complex challenges.
Unisys offers an outstanding benefits package, featuring unlimited paid time off, a 401(k) plan, comprehensive healthcare, HSA matching, ongoing learning opportunities, and more! We’re committed to supporting work-life balance and investing in your future success.
At Unisys, we incorporate video interviews as a key part of our hiring process. This allows us to get to know you better and provide a more engaging and convenient interview experience. We appreciate your understanding and look forward to connecting with you virtually!
#LI-AC1
This role may require access to export-controlled commodities and technology. Therefore, to conform to U.S. export control regulations, applicant should be eligible for any required authorizations from the U.S. Government.
Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.
This commitment includes our efforts to provide for all those who seek to express interest in employment the opportunity to participate without barriers. If you are a US job seeker unable to review the job opportunities herein, or cannot otherwise complete your expression of interest, without additional assistance and would like to discuss a request for reasonable accommodation, please contact our Global Recruiting organization at [email protected] or alternatively Toll Free: 888-560-1782 (Prompt 4). US job seekers can find more information about Unisys’ EEO commitment here .
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Unisys, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Unisys AI Hiring
Unisys has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Budapest, Budapest, Hungary.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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