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About This Role
Job Description:
The AI Adoption Manager drives the successful rollout, scaling, and sustained use of AI capabilities across the organization. This role focuses on enabling teams, reducing friction, building advocacy networks, and measuring adoption so that AI solutions translate into real business value through effective training, usability improvements, and continuous improvement.
Responsibilities:
Lead AI Enablement, Training \& Advocacy
Develop and execute programs that help departments understand, adopt, and effectively use AI capabilities.
- Drive training and enablement programs that improve understanding and use of AI tools and workflows
- Build, manage, and grow a champions network that accelerates adoption and shares best practices
- Promote awareness of AI capabilities in ways that connect technical functionality to business value
Drive Adoption Measurement \& Friction Reduction
Measure usage and identify barriers so adoption can scale effectively and sustainably.
- Track adoption metrics and success indicators across the organization
- Identify friction points preventing teams from leveraging AI solutions effectively
- Partner with stakeholders to improve usability, accessibility, and overall experience of AI\-driven tools
Scale Commercialization \& Continuous Improvement:
Create repeatable approaches for scaling AI usage, strengthening user value realization, and informing future improvements.
- Develop strategies and frameworks that support broad adoption and commercialization of AI capabilities
- Gather user and champions feedback to inform enhancements and improve outcomes over time
- Partner across product, operations, and business functions to ensure AI initiatives are not only launched, but actively adopted and delivering measurable value
Requirements:
- Bachelor's degree in Business, Marketing, Communications, Operations, Product, Organizational Development, or a related field, or equivalent work experience.
- 3–5 years of experience leading adoption, enablement, training, commercialization, or change management initiatives at scale.
- Hands\-on proficiency with modern AI tools and generative AI assistants (such as Claude, ChatGPT, or Copilot), including practical experience with effective prompting and a working understanding of what these tools can and cannot reliably do. A solid conceptual grasp of how AI capabilities apply to real business workflows is essential—deep technical or machine\-learning expertise is not required, but candidates must be credible, enthusiastic practitioners who can teach others and lead by example.
- Strong stakeholder engagement and community\-building skills, experience defining and tracking adoption metrics, and the ability to identify user friction and improve workflows or experience.
- Strong communication skills and the ability to translate technical AI capabilities into business value are essential.
- Experience operating in cross\-functional environments and driving measurable outcomes through continuous improvement is strongly preferred.
- Prior experience championing or scaling the adoption of AI tools within an organization is a significant plus.
As a forward\-thinking software provider, Computer Services, Inc. (CSI) helps community and regional financial institutions solve their customers’ needs through open and flexible technologies. In addition to its award\-winning core banking platform, these include the latest in lending, digital banking, payments, financial crime prevention and cybersecurity. Building on its 60\-year track record of personalized service, CSI is shaping the future of banking and empowering its customers to rival their competition. For more information about CSI, visit www.csiweb.com
CSI provides rewarding and challenging career opportunities for our employees. When determining your pay, we consider various factors such as your skills, qualifications, experience and location. Along with a competitive salary, this position includes eligibility for incentive awards based on both individual and business performance. We also offer a comprehensive range of benefits. To learn more about our benefits, visit: Benefits Summary
CSI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, physical and mental disability, marital status, veteran status, or any other characteristic protected by applicable law. If you need an accommodation during the recruitment process, please email us at [email protected] and we will work with you to meet your accessibility needs.
For applicants residing in California, please read Privacy Notice for California Residents \| CSI (csiweb.com)
Visa Sponsorship: We are unable to offer visa sponsorship for this position. Applicants must be authorized to work in the United States without the need for sponsorship now or in the future.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Computer Services Inc., 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Computer Services Inc. AI Hiring
Computer Services Inc. has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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