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About This Role
Job Description:
The AI Engineer II independently develops and supports AI engineering capabilities that enable reusable, secure, and scalable AI-assisted software delivery. This role builds components of the AI Harness, agent frameworks, evaluation systems, and developer workflow tools, while partnering with engineering teams to translate practical delivery needs into reliable AI-enabled solutions. The AI Engineer II contributes to production-quality AI applications and helps improve the consistency, safety, and usefulness of AI-supported engineering workflows.
Responsibilities:
Develop AI Harness & Platform Capabilities
- Design, build, test, and maintain well-defined AI platform components, services, connectors, and internal tools.
- Implement model routing, prompt execution patterns, API integrations, and reusable utilities aligned to platform standards.
- Improve reliability, performance, observability, and usability of AI engineering tools.
- Support production operations for AI-enabled services, including troubleshooting, monitoring, and issue remediation.
- May be responsible for on-call rotation and front-line incident response for AI-enabled production services, including triage, escalation, and remediation.
Build Reusable Agents, Skills & Connectors
- Develop reusable agents, skills, workflow components, and connectors that can be used across engineering teams.
- Implement agent behaviors, tool calls, structured workflows, and integration logic that support AI-assisted SDLC use cases.
- Apply standards for modularity, permissions, traceability, and maintainability.
- Partner with more senior engineers to refine shared libraries and reduce duplicate AI engineering work across teams.
Implement Evaluation, Testing & LLMOps Workflows
- Build and maintain evaluation workflows for prompts, agents, tools, and AI-generated outputs.
- Develop automated tests, regression checks, quality benchmarks, and validation approaches for AI-enabled capabilities.
- Support versioning, release management, rollback readiness, and lifecycle management for prompts, agents, and AI components.
- Analyze model and agent behavior to identify quality, cost, latency, security, or reliability concerns.
Apply AI Security & Governance Controls
- Embed approved logging, access control, data classification, and risk controls into AI-enabled workflows.
- Work with security and governance partners to address implementation requirements for responsible AI use.
- Validate that AI tools and agents are handling sensitive data appropriately and operating within defined guardrails.
- Support remediation of security, compliance, or audit findings related to AI engineering solutions.
Partner with Engineering Teams on AI-Assisted Delivery
- Collaborate with software engineering teams to understand delivery pain points and identify AI-enabled workflow improvements.
- Build examples, templates, and golden-path patterns that accelerate adoption of shared AI engineering capabilities.
- Support onboarding, technical guidance, and issue resolution for engineering users of AI platform tools.
- Contribute to team standards, documentation, and communities of practice around AI-assisted development.
Requirements:
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, or a related technical field, or equivalent work experience, with typically 5+ years of experience in software engineering, AI engineering, ML engineering, platform engineering, automation, or developer productivity roles.
- Strong programming skills are required, preferably in Python and one or more modern application development languages.
- Candidates should have experience building APIs, services, integrations, automated tests, and cloud-based applications.
- Experience with LLM application patterns, prompt engineering, RAG, agent frameworks, LLMOps, evaluation techniques, or AI-assisted development tools is strongly preferred.
- Candidates should demonstrate sound engineering judgment, strong collaboration skills, and the ability to independently deliver reliable technical solutions in a secure environment.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Computer Services, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Computer Services AI Hiring
Computer Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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