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Overview:
An AI Security Architect is responsible for designing and implementing robust security frameworks for AI systems, ensuring their protection against adversarial attacks, data breaches, and ethical issues. This role involves developing secure deployment strategies for AI models, particularly focusing on generative AI's unique risks, such as content generation and privacy concerns. They work closely with data scientists and engineers to integrate security measures throughout the AI lifecycle, from development to deployment. Additionally, the AI Security Architect ensures compliance with regulatory standards and promotes ethical AI use, maintaining the integrity and trustworthiness of AI solutions. Responsibilities:
- Lead AI working groups to design cybersecurity processes that enhance the enterprise’s ability to develop and implement secure AI solutions, aligning with business, technological, and threat considerations.
- Work with AI architects and developers to integrate security practices into the AI development lifecycle.
- Develop and implement data protection and AI security strategies to ensure the confidentiality, integrity, and availability of our data and AI systems throughout their lifecycle.
- Design and implement security solutions and controls tailored to the unique challenges of AI and ML applications.
- Conduct threat modeling exercises to identify potential security risks and vulnerabilities in AI systems, working closely with AI development teams to integrate security into the design and development processes.
- Monitor and analyze data protection and AI security metrics to identify areas for improvement and drive continuous enhancement of our processes and procedures.
- Utilize advanced testing methodologies and tools, such as adversarial testing, penetration testing, and vulnerability scanning, to assess the resilience and security posture of AI solutions against emerging threats and attack vectors.
- Lead investigations into security incidents and provide recommendations for preventive measures.
- Lead internal efforts to incorporate AI capabilities within the security organizations to enhance existing capabilities of security platforms and processes.
- Ensure AI systems comply with relevant national and state data protection and privacy laws.
- Apply security standards and frameworks, such as HITRUST and NIST to AI projects.
- Stay current on the latest data protection and AI security best practices and regulations, and proactively make recommendations for improvement.
- Collaborate with cross-functional teams to ensure data protection and AI security measures are integrated into all aspects of our AI systems and processes. Provide guidance and support to project teams to ensure that data protection and AI security requirements are met throughout the project lifecycle.
- Complete all responsibilities as outlined in the annual performance review and/or goal setting.
- Complete all special projects and other duties as assigned.
- Must be able to perform duties with or without reasonable accommodation.
This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.
Qualifications:
- Bachelor’s degree in computer science, Cybersecurity, Data Science, or a related field.
- Minimum of 12 years of experience in Cybersecurity domains including application security, security architecture, network security, and data protection.
- Strong knowledge of AI technologies and governance principles and their inherent security risks, machine learning models, adversarial attacks, and data privacy regulations.
- Excellent analytical skills to identify and address security vulnerabilities in AI systems
- Excellent communication and leadership skills to work across diverse teams.
- Relevant certifications like CISSP, CISM, CRISC, or AI ethics/security credentials are a plus.
- We are looking for a driven individual with a deep understanding of data protection and AI security best practices, as well as a strong background in cybersecurity
Mental Requirements:* Communicating with others to exchange information.
- Problem-solving and thinking critically.
- Works independently with little supervision.
- Ability to lead projects with little guidance
- Interpreting data.
- Making timely decisions in the context of a workflow.
- Maintaining focus.
- Remembering and adhering to processes and protocols.
- Applying established protocols in a timely manner.
Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands and/or fingers.
- Must be able to provide high-speed internet access / connectivity and office setup and maintenance.
- Must be able to provide a dedicated, secure work area.
Base compensation ranges from $170,000 to $212,000. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This role is eligible for discretionary bonus consideration
Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page.
Since this job will be based remotely, all interviews will be conducted virtually.
Date of posting: 10/8/2026 Applications are assessed on a rolling basis. We anticipate that the application window will close on 1/8/2027, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
#LI-REMOTE #LI-LL1 #senior
Salary Context
This $170K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cotiviti, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($191K) sits 10% below the category median. Disclosed range: $170K to $212K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cotiviti AI Hiring
Cotiviti has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Remote, US. Compensation range: $175K - $212K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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