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About This Role
Job Overview
ONLY US CITIZENS. REMOTE.
We are seeking a highly skilled and strategic IT Compliance \& AI Governance Consultant to partner directly with our client CTO office. In this role, you will be the foundational architect responsible for updating, scaling, and managing our corporate IT security frameworks and data governance models.
Your primary mandate will be to bridge departmental silos, conducting deep\-dive discovery across the organization to map data flows and establish an accurate data inventory. You will ensure our technology ecosystem safely accommodates, governs, and scales Artificial Intelligence (AI) and Machine Learning (ML) initiatives, while maintaining bulletproof alignment with global security and privacy standards.
Key Responsibilities
1\. Cross\-Functional Discovery \& Data Inventory
- Departmental Interviews: Conduct structured interviews and workshops with various department heads (e.g., Product, Engineering, Marketing, HR, Legal, and Sales) to comprehensively audit, discover, and document the flow of structured and unstructured data across the organization.
- Data Flow Mapping: Build and maintain an enterprise\-wide data inventory and data lineage map, specifically identifying where sensitive data is stored, how it is ingested, and how it migrates across different systems.
- Shadow IT \& AI Detection: Proactively identify and catalogue unauthorized "shadow" AI tools, SaaS platforms, and data repositories currently utilized by various business units.
2\. AI \& Data Governance Framework Architecture
- Adapt Data Frameworks: Redesign and expand the existing enterprise data governance framework to address specific AI risks (e.g., data lineage, synthetic data usage, retrieval\-augmented generation (RAG) pipelines, and model training inputs) discovered during the inventory process.
- Ethical \& Responsible AI: Establish policies surrounding algorithmic fairness, bias mitigation, explainability (XAI), transparency, and intellectual property (IP) protection regarding generative AI.
- Data Lifecycle Management: Define clear rules for data classification, minimization, retention, and isolation, specifically ensuring proprietary data is not leaked into public LLM training sets.
3\. Security \& Compliance Integration
- Framework Alignment: Manage, maintain, and map IT controls across core security frameworks such as SOC 2 Type II, ISO/IEC 27001, and NIST CSF.
- Incorporate AI Security Standards: Integrate emerging AI security frameworks, specifically ISO/IEC 42001 (Artificial Intelligence Management System) and the NIST AI Risk Management Framework (AI RMF), into the broader corporate compliance program.
- Regulatory Mapping: Ensure continuous adherence to evolving global regulations, including GDPR, CCPA/CPRA, and emerging AI\-specific laws (e.g., the EU AI Act).
4\. Risk Assessment \& Third\-Party Oversight
- AI Risk Assessments: Conduct comprehensive impact assessments on all internal and product\-facing AI deployments to identify security vulnerabilities, potential model drift, and compliance gaps.
- Vendor Vetting: Evaluate third\-party AI vendors, APIs, and SaaS tools. Formulate a vetting protocol to approve or deny incoming AI tech stacks based on security and data privacy mandates.
Required Qualifications \& Skills
Experience \& Education
- Experience: 3–6\+ years of experience in IT compliance, information security auditing, data governance, or technology risk management.
- AI/ML Familiarity: Minimum 1–2 years of hands\-on experience dealing with data privacy/governance issues explicitly related to cloud\-native environments, big data pipeline architecture, or AI/ML model deployment.
- Education: Bachelor’s degree in information technology, Cybersecurity, Legal/Compliance, Data Science, or a related field (Master’s or JD a plus).
Technical \& Leadership Skills
- Stakeholder Management \& Discovery: Proven ability to interview diverse department leads, translate complex technical data workflows from engineering, and extract operational data usage habits from non\-technical business units.
- Framework Expertise: Deep expertise in mapping controls for SOC 2, ISO 27001, and NIST. Strong familiarity with ISO/IEC 42001 and the NIST AI RMF.
- Technical Literacy: Ability to understand data infrastructure, API integrations, LLM guardrails, and access control models (RBAC/ABAC).
Preferred Certifications (One or more)
- IAPP Certified AI Governance Professional (AIGP) (Highly preferred).
- CISA (Certified Information Systems Auditor) or CRISC (Certified in Risk and Information Systems Control).
- CIPP/US/E (Certified Information Privacy Professional).
- CDMP (Certified Data Management Professional).
Pay: $60\.00 \- $63\.00 per hour
Work Location: Remote
Salary Context
This $124K-$131K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Global KTech, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($127K) sits 29% below the category median. Disclosed range: $124K to $131K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Global KTech AI Hiring
Global KTech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $131K - $131K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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