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About This Role
Job Description:
The VP \- AI Engineering owns the central AI engineering platform — the “AI Harness” — together with the agent frameworks, evaluation infrastructure, and AI\-assisted development practices that every CSI engineering team builds on. A small central team builds the platforms, connectors, and plumbing across LLMs, data, knowledge, and workflows; functional teams build on top of them. This role is accountable for a hard delivery\-velocity number, for the engineering bar on AI\- and agent\-generated code, and for the developer enablement that makes adoption real rather than nominal.
Own the delivery\-velocity mandate
- Set and hit the \~50% delivery\-acceleration target: establish the Year\-0 baseline and the DORA\-style metrics (lead time for change, deployment frequency, change\-failure rate, time to restore), and report progress to the CDAO and pillar peers.
- Translate individual adoption into organizational throughput — redesign team scope and workflow around human judgment plus agent execution, not individual task completion.
Build the AI Harness as a product
- Own the strategy and roadmap for the central AI platform — model routing, connectors, MCP servers, and the build\-vs\-buy framework that decides when to use Bedrock or Foundry rather than direct LLM access.
- Treat the Harness as a product, not infrastructure — named services, SLAs, adoption metrics, and a backlog driven by engineering\-team needs — while managing token and cost optimization across LLM usage.
Empower developers and agents
- Build and maintain the agent framework and the shared library of skills, connectors, and agents; set the standards for agent and connector architecture so teams compose rather than rebuild.
- Establish the target SDLC and common AI code / delivery tooling; define golden paths and the guardrails, gates, and review patterns that make AI\-assisted and agentic development safe at bank\-grade standards.
Evaluation, quality \& security
- Own eval and LLMOps infrastructure — versioning, regression and faithfulness testing — as the gate for shipping AI capabilities.
- Embed AI security, data\-classification enforcement, audit / logging, and use\-case risk review at the platform layer, in close partnership with Information Security and governance.
Enablement \& adoption
- Own the tooling\-access strategy and the enablement engine — onboarding, office hours, and change management — so adoption is measured and real, not nominal. The unlock is a real result on day one, not training.
- Partner with the Product Operations and other teams to keep practices current and capture institutional knowledge before it is lost.
Organization \& leadership
- Build, develop, and lead a high‑performing organization of delivery and operational leaders, fostering accountability, continuous learning, and readiness for new products and enhancements.
- Partner cross‑functionally to align with company strategy, product evolution, and enterprise standards.
10\+ years of experience in senior leadership of an AI/ML platform, applied\-AI, or engineering\-productivity organization at meaningful scale, with accountability for delivered outcomes — not research output alone. A track record of raising software delivery velocity (lead time, deployment frequency, change\-failure rate) through platform and developer\-experience investment. Deep, current fluency in LLM application engineering — agent frameworks, RAG, evals / LLMOps, prompt and model lifecycle, and cost / latency management at production scale. Hands\-on experience operationalizing AI\-assisted and agentic software development with the quality and security guardrails that make it safe. Experience operating to bank\-grade security, compliance, and audit requirements (financial services, fintech, or comparably regulated). Preferred experience in building an internal AI agent platform consumed by multiple product teams — with capability boundaries, permissioning, state / memory across sessions, and rollback for agent\-caused failures. Explicit build\-vs\-buy judgment across the model\-platform landscape (Amazon Bedrock, Azure AI Foundry, Anthropic Claude, Microsoft Copilot, Atlassian Rovo). Experience standing up MCP\-based connector and agent ecosystems. Familiarity with the community\-bank and credit\-union market and the competitive set (Fiserv, FIS, Jack Henry, Q2, Alkami).
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.
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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