AI/ML Lead

ISACA

. Provide hands-on engineering for development, integration, and production delivery of ISACA’s AI solutions .

Posted 10/16/2026full-timeUnited StatesSenior💰 $151,047 - $241,653 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and integrating AI solutions, particularly LLM-powered applications, with a strong focus on MLOps practices and cloud environments. Proven ability to lead engineering efforts, mentor teams, and communicate effectively with stakeholders across various domains.

Highest-signal resume keywords

AI Solution DevelopmentMLOps PracticesPython ProficiencyKnowledge Graph ArchitectureStakeholder Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI IntegrationLLM Application FrameworksRAG Pipeline Performance TrackingEmbedding-Based Semantic RetrievalVector DatabasesPrompt EngineeringCI/CD ImplementationContainerized DeploymentModel VersioningDrift Detection

Soft Skills

MentoringCollaborationCommunication

Tools & Technologies

AzureAzure Data FactoryApache AirflowSalesforceSitecore

Certifications & Qualifications

AI CertificationsAzure CertificationsAWS CertificationsGoogle Cloud Certifications

Industry Keywords

Knowledge Graph OntologyAI Vendor ManagementAgile DeliveryData ArchitectureResponsible AI

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudPythonSitecore

About the role

Key responsibilities & impact

Provide hands-on engineering for development, integration, and production delivery of ISACA’s AI solutions
Build and maintain knowledge graph ontology and configuration, embedding and retrieval pipelines, recommendation logic, and LLM-powered and agentic application components
Implement APIs and service contracts connecting AI capabilities to digital experiences and business systems
Review external vendor architecture, code, pull requests, and deliverables against technical requirements and engineering standards
Define acceptance criteria and support production-readiness validation
Lead knowledge transfer from vendors to internal ownership and reduce vendor dependency
Implement and operate MLOps and LLMOps practices, including CI/CD, versioning, evaluation, monitoring, drift detection, and token cost tracking
Establish code review, documentation, runbook, and reusable-component practices
Implement security, responsible AI, and auditability controls
Build ingestion and transformation pipelines from CRM, content and learning platforms, workforce data, and market trend data
Implement data contracts, quality checks, and monitoring with source-system owners
Identify and close data architecture gaps with data, engineering, and product teams
Serve as the first engineer on the AI implementation team; recommend hiring sequence, participate in hiring and onboarding, and mentor engineers and contractors
Communicate AI decisions, technical risks, and tradeoffs to product, engineering, and IT stakeholders
Collaborate across product, architecture, security, and data teams
Evaluate emerging AI tools, platforms, and architectures through experimentation and proofs of concept

Requirements

What you’ll need

Bachelor’s Degree in Computer Science, Data Science, Statistics, Engineering, or a related field, or equivalent professional experience
8+ years of professional software, data, or application development experience
2+ years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions in production environments
Production delivery of LLM-powered or AI solutions in cloud environments; Azure strongly preferred or equivalent
Proficiency in Python and LLM application frameworks and SDKs
Working knowledge of LLM evaluation, RAG pipeline performance tracking, and prompt versioning
Hands-on experience with RAG pipelines, prompt engineering, embedding-based semantic retrieval, vector databases, and LLM orchestration
Hands-on experience with knowledge graph architectures and ontology design; Graphwise experience is a strong plus
Foundational MLOps and LLMOps capability, including CI/CD, containerized deployment, model and prompt versioning, monitoring, token cost tracking, and drift detection
Demonstrated ability to oversee AI vendor work through architecture review, acceptance criteria, and delivery validation
Strong stakeholder communication skills
Experience in agile delivery environments with cross-functional product, engineering, data, and security teams
Preferred master’s degree in Computer Science, Machine Learning, Data Science, or Artificial Intelligence
Preferred 10+ years of professional software or data engineering experience, including 3+ years of hands-on AI experience
Familiarity with multi-agent frameworks, Azure Data Factory or Apache Airflow, and enterprise platforms such as Salesforce and Sitecore is a plus
Preferred AI, Azure, AWS, or Google Cloud certifications listed in the posting
Commitment to ISACA’s mission, purpose, and values; cultural sensitivity appropriate for a global professional membership organization

Benefits

Comp & perks

Periodic onsite attendance may be required based on business needs
Occasional travel to company-sponsored events, meetings, all-hands gatherings, training sessions, or other necessary activities
Equal employment opportunity and inclusive workplace commitment