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About This Role
Description:
This position is responsible for designing, developing, implementing, and maintaining artificial intelligence (AI) solutions that support and enhance the Ministry’s operational efficiency, decision\-making capabilities, and digital innovation initiatives. The AI Specialist will work across departments to identify opportunities for automation, data\-driven insights, and intelligent system integration while ensuring ethical, secure, and scalable AI practices. This role also requires a blend of technical expertise in AI/ML technologies, strong communication skills, and the ability to translate complex concepts into practical ministry applications.
PRIMARY DUTIES AND RESPONSIBILITIES:
- Design, develop, and deploy AI/ML models to support business and ministry objectives.
- Work the IT Director to plan and design AI automations
- Identify opportunities to implement AI\-driven automation across organizational processes.
- Build, train, test, and optimize machine learning and deep learning models.
- Collaborate with IT, data, and business teams to define AI requirements and use cases.
- Develop and maintain data pipelines for AI model training and inference.
- Design, develop, and deploy AI and machine learning solutions including natural language processing, computer vision, recommendation systems, and generative AI applications.
- Integrate AI capabilities into existing ministry systems such as content management, customer relationship management (CRM), broadcasting, and digital outreach platforms.
- Develop and maintain AI\-driven automation workflows to streamline repetitive tasks across departments, including data entry, scheduling, correspondence, and reporting.
- Build and manage prompt engineering strategies, fine\-tuning approaches, and retrieval\-augmented generation (RAG) pipelines for ministry\-specific use cases.
- Collaborate with content, media, and communications teams to leverage AI for content creation, transcription, translation, summarization, and personalization.
- Monitor AI model performance, accuracy, and ethical compliance; implement feedback loops and continuous improvement processes.
- Establish and enforce data governance, privacy, and security best practices as they relate to AI systems and the data they consume.
- Monitor model performance and implement improvements for accuracy, scalability, and efficiency. · Ensure data quality, governance, and compliance with security standards.
- Research and recommend emerging AI technologies, tools, and frameworks.
- Develop natural language processing (NLP), computer vision, or predictive analytics solutions as needed.
- Create and maintain documentation for AI systems, models, and processes.
- Provide technical guidance and training to team members on AI\-related tools and practices.
- Assist in the evaluation and implementation of AI platforms such as Azure AI, AWS AI/ML, or other cloud\-based services.
- Ensure responsible AI usage, including bias mitigation, transparency, and ethical considerations.
- Participate in cross\-functional projects to enhance digital transformation initiatives. · Support troubleshooting and resolution of AI\-related system issues.
- Stay current with advancements in AI, machine learning, and data science.
- May require participation in events or initiatives that involve AI\-enabled systems.
- Other duties as assigned by management.
Requirements:
PREFERRED EXPERIENCE:
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent work experience.
- 7\+ years of hands\-on experience designing, building, and deploying AI/ML solutions in a professional environment.
- Demonstrated proficiency with AI/ML frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, LangChain, or similar.
- Experience with cloud\-based AI services including Azure AI, AWS SageMaker, Google Vertex AI, or OpenAI API.
- Strong programming skills in Python; experience with JavaScript, SQL, and API development is a plus.
- Experience with large language models (LLMs), prompt engineering, fine\-tuning, and retrieval\-augmented generation (RAG).
- Familiarity with data engineering concepts including ETL pipelines, data warehousing, and database management.
- Experience with automation and integration platforms such as Power Automate, Zapier, or Make.
- Relevant certifications in AI/ML (e.g., AWS Machine Learning Specialty, Google Professional ML Engineer, Microsoft Azure AI Engineer) preferred.
- Experience working in a nonprofit, ministry, or media\-driven organization is a plus.
KNOWLEDGE, SKILLS, AND ABILITIES:
- Strong understanding of machine learning algorithms, deep learning architectures, natural language processing, and computer vision.
- Proficiency in data analysis, statistical modeling, and data visualization tools such as Power BI, Tableau, or Python\-based libraries.
- Solid understanding of AI ethics, bias detection and mitigation, and responsible AI deployment practices.
- Experience with version control systems (Git), CI/CD pipelines, and containerization technologies (Docker, Kubernetes).
- Excellent written and verbal communication skills with the ability to explain technical AI concepts to non\-technical stakeholders.
- Strong project management and organizational skills; ability to manage multiple AI initiatives simultaneously.
- Ability to learn and implement new technologies independently by utilizing readily available resources (documentation, research papers, online courses, etc.).
- On\-call availability: must be available on nights, weekends, and holidays as needed to support AI systems in a 24x7 environment.
- High level of analytical and critical thinking skills to solve complex problems.
- Confident, self\-starting, innovative, and goal oriented.
EQUIPMENT TO BE USED:
- Standard office equipment and modern AI development tools, platforms, and computing environments.
TYPICAL PHYSICAL DEMANDS:
- Ability to sit and work at a computer for extended periods.
- Occasional standing, walking, bending, and lifting (up to 20–30 lbs.).
- Requires normal range of hearing and vision.
TYPICAL MENTAL DEMANDS:
- Ability to analyze complex datasets and systems to derive insights and solutions.
- Must be highly organized and detail oriented.
- Ability to work with complex technical concepts and adapt to evolving technologies.
- Strong problem\-solving and decision\-making skills.
- Ability to prioritize and manage multiple concurrent tasks. · Must communicate effectively in both written and verbal formats.
- Ability to collaborate across teams and interact with individuals at all levels.
WORKING CONDITIONS:
- Works in a normal/typical office environment with minimal supervision.
- Adherence to the Ministry’s policies, procedures, and standards is required.
- Team\-oriented environment requiring collaboration and professionalism.
OTHER:
- Born again believer and must adhere to the doctrines of this organization as upheld by Kenneth and Gloria Copeland and their appointed representatives.
- Must work well with others as a team and maintain unity.
- Must maintain a good attendance record.
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 Kenneth Copeland Ministries, 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.
Kenneth Copeland Ministries AI Hiring
Kenneth Copeland Ministries has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newark, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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