Interested in this AI/ML Engineer role at ISACA?
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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/ML Lead is a senior, hands-on engineer and the first member of ISACA's AI implementation team, a new internal capability that will take on AI engineering work currently delivered by external vendors. Reporting to the VP, Application Engineering, and working within the platform architecture, standards, and governance set by the Senior Director, AI Architecture, Platforms & Agentic Systems, this role builds and hardens the AI solutions that serve ISACA's members and business, both those in delivery today and those that emerge as ISACA's AI portfolio grows. The initial focus is ISACA's personalized member experience, a production RAG and knowledge graph-based recommendation system that integrates ISACA's Body of Knowledge, certification details, workforce skills data, and member profile information. The majority of the role is spent implementing: writing production code, building data and retrieval pipelines, and operationalizing the engineering practices that keep AI systems reliable. Success is measured by production stability, the share of AI engineering work brought in-house, and the implementation team's growing ability to deliver ISACA's current and future AI needs without vendor dependency. Responsibilities: AI Platform Technical Contribution* Provide hands-on engineering for the development, integration, and production delivery of ISACA's AI solutions, beginning with the AI-driven member experience, working within the platform architecture and standards set by the Senior Director, AI Architecture. Build and maintain core AI components, including knowledge graph ontology and configuration (Graphwise or a comparable enterprise knowledge graph platform), embedding and retrieval pipelines, recommendation logic, and LLM-powered and agentic application components. Implement the APIs and service contracts that connect AI capabilities to ISACA's digital experiences and business systems. Ensure all components are built for reuse, long-term internal ownership, and maintainability.
AI Vendor Oversight & Insourcing* Serve as the key internal technical counterpart to external AI development vendors and contractors. Participate in vendor architecture reviews and review vendor code and pull requests, evaluating AI solution designs, including RAG pipelines, knowledge graph implementation, and LLM integration, against ISACA's technical requirements and the engineering standards set by the Senior Director, AI Architecture. Define and maintain acceptance criteria for vendor deliverables, support production readiness validation, and surface technical risk or delivery misalignment to the Senior Director, AI Architecture, and IT leadership. Lead knowledge transfer so that vendor-built components move to internal ownership on a planned timeline, progressively reducing vendor dependency.
AI Engineering Practices & Operations* Implement and operate MLOps and LLMOps practices in line with the platform standards set by the Senior Director, AI Architecture, including CI/CD for ML and LLM pipelines, model and prompt versioning, evaluation, monitoring, drift detection, and token cost tracking. Establish team-level engineering practices, including code review, documentation, runbooks, and reusable components, that promote quality and consistency across the implementation team and sustain ISACA's AI solutions beyond any single initiative. Foster knowledge sharing so that expertise is built into the organization rather than held by individuals. Implement the security, responsible AI, and auditability controls defined by the Senior Director and Security within the AI solutions the team builds.
Data Infrastructure & AI Data Enablement* Build and maintain the data foundations that supply ISACA's AI solutions with reliable, well-structured inputs. Implement ingestion and transformation pipelines from source systems such as CRM, third-party content and learning platforms, and external workforce and market trend data. Implement data contracts with source-system owners, plus data quality checks and monitoring aligned to the standards set by the Senior Director, AI Architecture. Identify and document gaps in ISACA's current data architecture that limit AI performance or extensibility, and work with data, engineering, and product teams to close them.
AI Implementation Team Building* Serve as the first engineer on ISACA's AI implementation team. Recommend roles and hiring sequence to bring outsourced AI engineering in-house, participate in hiring and onboarding, and mentor engineers and contractors.
Stakeholder Communication* Translate AI system decisions, technical risks, and engineering tradeoffs into clear, actionable communication for product, engineering and IT stakeholders. Collaborate across product, architecture, security, and data teams to ensure AI initiatives integrate coherently into ISACA’s broader enterprise.
Other Duties* Evaluate emerging AI tools, platforms, and architectures through hands-on experimentation and proofs of concept, providing practical recommendations that support the Senior Director's build-versus-buy and platform decisions.
Qualifications: Required Field of Study:* Bachelor’s Degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience.
Minimum Years of Experience Required:* 8+ years of professional software, data, or application development experience, including 2+ years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions in production environments, beyond proof-of-concept work.
Description of Minimum Relevant Experience Required:* Production delivery of LLM-powered or AI solutions in cloud environments (Azure strongly preferred or equivalent), including experience with LLM integration, RAG pipeline architecture, and model evaluation beyond proof-of-concept work.
- Proficiency in Python and LLM application frameworks and SDKs; working knowledge of LLM evaluation, RAG pipeline performance tracking, and prompt versioning practices using tools such as Microsoft Foundry, Azure Promptflow, or equivalent evaluation frameworks.
- Hands-on experience with retrieval-augmented generation (RAG) pipelines, prompt engineering, embedding-based semantic retrieval, vector databases (e.g., Azure AI Search, Pinecone, or Weaviate), and LLM orchestration. Familiarity with multi-agent frameworks (e.g., LangChain, Microsoft AutoGen, or comparable) is a plus.
- Hands-on experience with knowledge graph architectures and ontology design; direct Graphwise experience is a strong plus. Working knowledge of Neo4j, Amazon Neptune, or Azure Cosmos DB provides transferable graph reasoning skills and is helpful.
- Foundational MLOps and LLMOps capability: CI/CD for ML and LLM pipelines (Azure DevOps or GitHub Actions), containerized deployment (Docker/Kubernetes), model and prompt versioning, monitoring, token cost tracking, and drift detection. Familiarity with data and workflow pipeline orchestration tools such as Azure Data Factory or Apache Airflow is a plus.
- Demonstrated ability to oversee AI vendor work through technical architecture review, acceptance criteria definition, and delivery validation.
- Strong stakeholder communication skills: proven ability to translate AI architecture decisions, risks, and tradeoffs for non-technical audiences including product and engineering stakeholders.
- Experience working in agile delivery environments with cross-functional product, engineering, data, and security teams.
Preferred Field of Study:* Masters Degree in Computer Science, Machine Learning, Data Science, or Artificial Intelligence
Preferred Years of Experience:* 10+ years of professional software or data engineering experience, including 3+ years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions, ideally including early-stage or greenfield AI systems delivered at enterprise scale.
Description of Preferred Relevant Experience:* Experience contributing to or helping shape an AI or data practice — establishing engineering practices and delivery patterns. Experience helping build or scale an engineering team is beneficial.
- Domain literacy in IT governance, cybersecurity risk management, or professional certification ecosystems — sufficient to collaborate effectively with ISACA's internal subject-matter experts and align AI outputs to organizational standards.
- Experience with knowledge graph reasoning, or GraphRAG patterns relevant to professional knowledge and competency ontology use cases.
- Familiarity with bias and fairness evaluation for AI-driven recommendation systems, including output-level testing for demographic or professional skew
- Prior experience working in a non-profit, professional association, standards body, or other mission-driven organization is a plus.
- Experience collaborating across hybrid delivery models including in-house staff, contractors, and external development vendors.
- Proficiency with Azure AI platforms, or equivalent services on AWS or GCP. Azure is the primary cloud platform for this role.
- Familiarity with enterprise platforms commonly used in ISACA's ecosystem, such as Salesforce, Sitecore, and Microsoft Azure environments, is a plus.
- Working familiarity with responsible AI principles and general awareness of relevant AI regulatory trends.
Certification and Licensing Preferred:* Microsoft Certified: Azure AI App and Agent Developer Associate (AI-103) or MLOps Engineer Associate (AI-300); prior Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) also recognized
- AWS Certified Generative AI Developer – Professional or Machine Learning Engineer – Associate, or Google Cloud Professional Machine Learning Engineer
Competencies/Skills Required:* Strong customer and member focus, with the ability to design and deliver AI systems that reflect ISACA’s commitment to professional excellence and digital trust
- A senior hands-on engineer who writes production code, reviews vendor work, and helps grow an internal implementation team.
- Intellectual adaptability and continuous learning orientation; ability to rapidly develop fluency in new architectures and tools as they emerge.
- Strong problem-solving and analytical thinking; ability to evaluate tradeoffs between innovation, delivery constraints, cost, and risk in early-stage AI programs
- Excellent oral and written communication skills, with the ability to explain AI architecture decisions, technical risks, and engineering tradeoffs to technical and product stakeholders
- Collaborative working style, with the ability to build alignment across engineering, product, data, and security teams without direct authority.
- Self-starter capable of operating autonomously in environments with evolving requirements and incomplete information
- Commitment to ISACA’s mission, purpose, and values; cultural sensitivity and awareness appropriate for a global professional membership organization.
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 $151,047.00 - USD $241,653.00 /Yr. Benefits Information:
Benefits Information available below: ISACA Career Opportunities and Benefits
Salary Context
This $151K-$241K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 7% below the category median. Disclosed range: $151K to $241K.
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.
ISACA AI Hiring
ISACA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $241K - $241K.
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.
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