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About This Role
Applications must be submitted no later than 11:59 p.m. CT on the day before the listed closing date.
Weekly Work Hours
40Compensation Range
Staff Range N09Salary Minimum
$100,636.00 AnnuallyFLSA
United States of America (Exempt) Work Location
- All positions are considered on-site roles.
- After six months of employment, some positions may be eligible to apply for a hybrid work arrangement based on job responsibilities, operational needs, supervisor approval, and Dallas College policy.
- Academic (Faculty) positions require an on-campus presence, even when teaching online courses, in accordance with state law.
Position Type
Staff Position / Job Summary
Serves as the Office of Finance's lead analytical resources overseeing the Data & Artificial Intelligence Analytics Department. Responsible for team leadership and development, advanced financial modeling, forecasting, strategic budget analysis, and AI-enabled process improvement. The position leads complex analytical initiatives that support institutional planning while mentoring analysts and evaluating innovative technologies that improve Finance operations.
Essential Duties & Responsibilities
- Develop advanced forecasting models supporting annual and multi-year budget planning.
- Analyze revenues, expenditures, staffing, enrollment, tuition, and other financial drivers.
- Perform scenario analysis, financial modeling, and sensitivity analyses to support executive decision-making.
- Evaluate financial impacts of legislative changes, economic trends, and institutional initiatives.
- Conduct complex financial and operational analyses.
- Design dashboards, executive reports, and key performance metrics.
- Translate complex financial data into actionable recommendations for Finance leadership.
- Identify trends, risks, and opportunities that influence financial performance.
- Research, evaluate, and pilot AI capabilities that improve financial analysis, reporting, forecasting, and business processes.
- Identify opportunities to automate recurring analyses and operational workflows.
- Assist in developing standards for responsible AI implementation and governance.
- Stay informed on emerging AI technologies and recommend practical applications.
- Mentor analysts and review analytical work for quality and consistency.
- Develop analytical standards, documentation, and best practices.
- Lead cross-functional analytical projects supporting Finance priorities.
- Required Dallas College Professional Development Hours per Academic Year.
- All employees are required to complete a minimum of 19 hours.
- People leaders are required an additional 16 hours of leadership development.
- Perform other duties not listed as assigned.
The following duties are required for People Leader roles specifically:
- Exercises full discretion and decision-making authority in the recruitment, selection, hiring, mentoring, coaching, development & termination of staff, ensuring alignment with organizational goals, budgetary constraints, and workforce planning strategies.
- Drive high performance by administering the performance management process timely, setting clear expectations, conducting performance evaluations, and implementing initiatives that enhance employee engagement, motivation, and retention.
Minimum and Preferred – Education, Experience, and/or Certifications
Required Qualifications for Role (necessary or mandatory)
- Bachelor's degree in Finance, Accounting, Economics, Business Analytics, Data Analytics, Information Systems, Public Administration, or related field. Official transcripts are required.
- Five (5) to seven (7) years of progressively responsible experience in business analysis and data modeling.
- Experience developing financial forecasts, long-range financial plans, and executive-level reporting.
- Experience implementing business intelligence tools, analytics platforms, or AI-enabled business solutions.
- Demonstrated supervisory experience or completed training.
- Must possess a current and valid Texas driver’s license.
- Experience developing and implementing [programs, services, and/or courses] that are designed to meet the needs of the increasingly diverse college community.
- Ability to maintain the security or integrity of the critical infrastructure of Dallas College.
Other Preferred Qualifications for Role (not mandatory, but advantageous)
- Power BI or Tableau or other modeling tools
- Excel (advanced)
- SQL (advanced query)
- ERP systems (Workday preferred)
- Microsoft Copilot
- Predictive analytics tools
- Bilingual or multilingual preferred
Knowledge, Skills, and Abilities
Required Qualifications for Role (necessary or mandatory)
- Understanding of governmental or higher education budgeting and finance principles.
- Advanced experience in financial forecasting, modeling, and scenario planning.
- Experience participating in budget development and resource allocation methodologies.
- Intermediate knowledge of business intelligence and performance measurement.
- Continuous learning of AI technologies, governance, ethics, and responsible use data governance and reporting best practices.
Work Condition Environment / Physical Requirements
Normal physical job functions performed within a standard office environment. Reasonable accommodation(s) may be made to individuals with physical challenges to perform the essential duties and responsibilities.
The intent of this job description is to provide a representative summary of the major duties and responsibilities performed by incumbents of this job and shall not be construed as a declaration of the total of the specific duties and responsibilities of any particular position. Incumbents may be directed to perform job-related tasks other than those specifically presented in this description. Position requires regular and predictable attendance.
About Us
Since 1965, Dallas College, formerly Dallas County Community College District, has served more than three (3) million students. Comprising seven campuses located around the Dallas/Fort Worth area, we are one of the largest community college systems in the state of Texas. We strive to be a leader in the community college space, placing students at the center of everything we do.
Dallas College is committed to cultivating an environment of opportunity and belonging for all students and employees. We recognize that the Dallas College workforce, and the diverse talent that stems from it, is directly linked to our success. We are part of an equal opportunity system that provides education and employment opportunities without discrimination on the basis of any protected attribute, including race, color, religion, national origin, sex, disability, age, sexual orientation, gender identity or gender expression, veteran status, pregnancy or any other basis protected under applicable law. In accordance with applicable law, Dallas College will make reasonable accommodations for applicants and employees’ religious practices and beliefs, as well as any mental health or physical disability needs.
Applications Deadline
October 26, 2026
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 Dallas College, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
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.
Dallas College AI Hiring
Dallas College has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Mesquite, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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