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About This Role
About ISACA: ISACA® (www.isaca.org) champions the global workforce advancing trust in technology. For more than 55 years, ISACA has empowered its community of 195,000\+ members with the knowledge, credentials, training and network they need to thrive in fields like information security, governance, assurance, risk management, data privacy and emerging tech. With a presence in more than 195 countries and with more than 230 chapters worldwide, ISACA offers resources tailored to every stage of members’ careers—helping them to thrive in a rapidly changing digital landscape, drive trusted innovation and ensure a more secure digital world. Through the ISACA Foundation, ISACA also expands IT and education career pathways, fostering opportunities to grow the next generation of technology professionals. Overview:
The AI Solutions Engineer is responsible for the development, integration, implementation, and maintenance of artificial intelligence (AI) and large language model (LLM) applications used to support internal business operations. This position develops AI\-enabled applications, integrations, and tools and connects AI capabilities with existing enterprise systems and data sources.
The position works with business and technology teams to translate functional requirements into technical solutions and supports AI applications throughout the development lifecycle, including design, development, testing, deployment, maintenance, and enhancement. The AI Solutions Engineer also ensures solutions are developed in accordance with applicable information security, privacy, data governance, and technology standards.
Responsibilities:
AI and Agentic Solution Development* Design, develop, test, deploy, and maintain LLM\-powered applications, integrations, and agentic AI solutions using Azure cloud technologies, Azure OpenAI, and related AI services.
- Develop Python\-based applications and services that support natural language processing, generative AI, and other AI\-enabled business use cases.
- Design and implement agentic workflows that orchestrate AI services, models, tools, and internal or external data sources to address defined business requirements.
- Develop and implement retrieval\-augmented generation (RAG), prompt engineering, semantic search, embeddings, and related techniques to support AI application functionality.
- Develop reusable application components, services, and technical patterns to support AI capabilities across internal applications.
- Develop prototypes and proofs of concept and transition approved solutions into production applications.
AI Integration, Performance and Optimization* Integrate Azure OpenAI APIs, AI services, and external models with internal applications, systems, and data sources.
- Design and maintain integrations with consideration for application reliability, scalability, performance, and maintainability.
- Develop and optimize prompts, model configurations, and parameters to improve application performance, response quality, and cost efficiency.
- Perform model configuration and fine\-tuning, as appropriate, based on application requirements and available technologies.
- Monitor, troubleshoot, and debug agentic workflows, application integrations, and AI\-generated outputs.
- Evaluate changes in LLM and AI technologies and recommend modifications or enhancements to existing applications and technical approaches.
AI Monitoring, Security \& Reliability* Develop and implement automated evaluation methods and frameworks to monitor the quality, accuracy, reliability, and performance of AI\-enabled applications.
- Monitor and evaluate AI\-generated outputs for hallucinations, performance degradation, and other quality or reliability concerns.
- Implement technical safeguards to mitigate prompt injection, inappropriate or harmful outputs, unauthorized access, and exposure of confidential or sensitive organizational data.
- Develop AI solutions in accordance with organizational information security, privacy, data governance, and technology requirements.
- Work with Information Security and other applicable functions to identify and address technical risks associated with AI applications and agentic workflows.
- Support ongoing monitoring and maintenance of AI solutions to identify and resolve application, model, integration, and performance issues.
Cross Functional Collaboration \& Solution Design* Collaborate with cross\-functional business and technology teams to translate business requirements into technical requirements and AI\-enabled solutions.
- Participate in requirements gathering, solution design, technical planning, and implementation activities.
- Communicate technical requirements, capabilities, limitations, dependencies, and risks to technical and non\-technical stakeholders.
- Coordinate with Information Technology, Information Security, Data, and other applicable teams regarding system integrations, data requirements, security controls, and deployment dependencies.
- Provide technical input regarding the feasibility and implementation requirements of proposed AI use cases.
Technical Documentation* Create and maintain technical documentation for application code, system architecture, integrations, configurations, agentic workflows, and technical dependencies.
- Document architecture decisions, technical approaches, development standards, and solution configurations to support ongoing maintenance and team knowledge sharing.
- Maintain documentation as applications, integrations, and technical requirements change.
Qualifications:
Required Field of Study:* Bachelor's degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence, or a related field
Minimum Years of Experience Required:* 5\+ years of professional software or application development experience, including hands\-on experience developing Python\-based applications or solutions, and experience developing, implementing, or integrating AI\-enabled applications.
Description of Preferred Experience:* Strong software/Python development foundation plus some hands\-on AI\-enabled application experience, with the more specialized AI/LLM/cloud experience remaining preferred.
- Experience integrating applications with APIs, enterprise systems, databases, or other data sources.
- Experience developing or supporting applications within a cloud\-based environment.
- Experience applying security and data protection practices to application development and system integrations.
Preferred Field of Study:* Advanced degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field.
Preferred Years of Experience:* 8\+ years of professional software or application development experience, including two (2\) or more years of hands\-on experience developing, implementing, or integrating AI, generative AI, LLM\-powered, or agentic AI solutions.
Description of Preferred Experience:* Experience developing or integrating AI and LLM solutions within a complex or enterprise environment.
- Experience with Microsoft Azure, Azure OpenAI, Azure AI services, or comparable cloud\-based AI platforms.
- Experience developing agentic AI workflows and orchestrating AI models, tools, APIs, and data sources.
- Experience with retrieval\-augmented generation (RAG), prompt engineering, embeddings, semantic search, or vector databases.
- Experience implementing security controls and safeguards for AI\-enabled applications.
- Experience developing evaluation or monitoring approaches for AI application performance, accuracy, hallucinations, or model drift.
- Experience optimizing LLM prompts, model configurations, or parameters for performance, reliability, and cost efficiency.
- Experience working within a complex, enterprise, international, or global organization
Competencies/Skills Required:* Strong written and verbal communication skills, with the ability to communicate technical concepts to both technical and non\-technical audiences.
- Strong programming and application development skills, including proficiency in Python.
- Working knowledge of artificial intelligence, generative AI, large language models (LLMs), and related development approaches, including prompt engineering, RAG, and agentic workflows.
- Working knowledge of cloud\-based technologies, AI services, information security, data privacy, and secure development practices.
- Strong critical thinking, analytical, troubleshooting, and problem\-solving skills.
- Ability to translate business requirements into practical, secure, and scalable technical solutions.
- Strong collaboration and stakeholder management skills, with the ability to influence and work effectively across functions without direct authority.
- High degree of initiative, accountability, adaptability, and sound judgment, with the ability to manage multiple priorities and work independently.
This position may require periodic onsite attendance, either currently or in the future, based on business needs. Occasional travel may also be required to attend company\-sponsored events, meetings, all\-hands gatherings, training sessions, or other activities necessary to support the essential functions of the role.
Equal Opportunity Employer (EEO): ISACA is proud to be an equal opportunity employer. ISACA is committed to building an environment of diversity, equity, and inclusion where equal employment opportunities are available to all applicants and employees without regard to race, color, religion, sex (including pregnancy and gender identity), national origin, age, ancestry, disability, genetic information, citizenship, sexual orientation, veteran status, marital status, familial status, military discharge status, or any other characteristic or status protected by federal, state, or local law. We support an inclusive workplace where employees excel based on merit, qualifications, experience, and ability. Posted Salary Range: USD $98,679\.00 \- USD $148,020\.00 /Yr. Benefits Information:
Benefits Information available below:
ISACA Career Opportunities and Benefits
Salary Context
This $98K-$148K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ISACA, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($123K) sits 43% below the category median. Disclosed range: $98K to $148K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ISACA AI Hiring
ISACA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $148K - $148K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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