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About This Role
Company Description
At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!
Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs. We are deliberate and self-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA.
Job Description THE POSITION:
The AI Security Engineer I supports the security, governance, and responsible use of AI, Machine Learning (ML), and Generative AI technologies across EVERSANA. This role assists with securing AI-enabled applications, models, and data; conducting security assessments; monitoring emerging AI threats; and implementing security controls aligned with organizational policies and regulatory requirements.
Working closely with both IT and business stakeholders, the AI Security Engineer I helps ensure AI solutions are developed, deployed, and operated securely while enabling innovation and managing risk effectively.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:
- Assist in the implementation and maintenance of security controls for Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions.
- Support security assessments of AI-enabled applications, models, and services to identify vulnerabilities, threats, and misconfigurations.
- Participate in threat modeling exercises for AI systems and help identify appropriate security controls and mitigations.
- Monitor emerging AI security risks, vulnerabilities, attack techniques, and industry trends, providing recommendations for risk reduction.
- Assist with testing and validation of AI security controls, including model protection, data security, and access management measures.
- Collaborate with security, data science, engineering, and compliance teams to ensure AI solutions align with organizational security standards, regulatory requirements, and responsible AI principles.
- Support governance initiatives related to AI usage, including model inventory management, risk assessments, and policy compliance activities.
- Help evaluate and implement security technologies and tools designed to protect AI systems, data pipelines, and cloud-native AI environments.
- Contribute to security investigations involving AI platforms and assist with incident response activities related to AI and emerging technologies.
- Participate in periodic testing of disaster recovery and business continuity plans for critical AI and business systems.
- Document findings, recommendations, and remediation activities in accordance with established security and compliance processes.
- Demonstrate a commitment to diversity, equity, and inclusion through continuous development, modeling inclusive behaviors, and proactively managing bias.
- Perform other duties as assigned.
Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by an employee with a disability, unless such accommodation would cause an undue hardship for EVERSANA. If reasonable accommodation is needed to perform the essential functions of your job position, please contact Human Resources.
EXPECTATIONS OF THE JOB:
- Travel – 15% annually
- Hours – Full-time, 5 days per week
The above list reflects the general details necessary to describe the expectations of the position and shall not be construed as the only expectations that may be assigned for the position.
An individual in this position must be able to successfully perform the expectations listed above.
Qualifications MINIMUM KNOWLEDGE, SKILLS AND ABILITIES:
The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.
- Bachelor's degree in Computer Science, Cybersecurity, Information Systems, Engineering, or related field, or equivalent experience.
- 1-3 years of experience in cybersecurity, cloud security, application security, AI engineering, machine learning operations (MLOps), or related technology disciplines.
- Strong analytical, troubleshooting, and problem-solving skills.
- Foundational understanding of AI/ML concepts, Large Language Models (LLMs), Generative AI technologies, and associated security risks.
- Familiarity with AI security topics such as prompt injection, model abuse, data poisoning, model theft, adversarial attacks, and secure AI development practices.
- Basic understanding of cloud platforms (Azure, AWS, or Google Cloud) and cloud security principles.
- Knowledge of security technologies such as EDR, SIEM, vulnerability management tools, identity and access management (IAM), and security monitoring solutions.
- Ability to communicate technical concepts effectively to both technical and non-technical stakeholders.
- Demonstrated ability to work collaboratively in cross-functional teams.
PREFERRED QUALIFICATIONS:
- Exposure to AI/ML development, MLOps platforms, or Generative AI technologies in a professional, academic, or personal project environment.
- Experience with cloud-native AI services such as Azure OpenAI, Azure AI Services, Anthropic Claude, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
- Familiarity with AI governance, responsible AI frameworks, NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, OWASP Top 10 for LLM Applications, or similar industry guidance.
- Basic scripting or automation experience using Python, PowerShell, or similar languages.
- Industry certifications such as: + Security+ + SC-900, AZ-900, or AI-900 + SC-200 or SC-300 + CCSK + GIAC Foundational Certifications + Other relevant cybersecurity, cloud, or AI security certifications
- Ability to demonstrate sound judgment, professionalism, and confidentiality.
- Excellent written and verbal communication skills.
- Demonstrated interest in AI security, emerging technologies, and continuous learning.
- Experience supporting cybersecurity projects, security assessments, or governance initiatives.
PHYSICAL/MENTAL DEMANDS AND WORKING ENVIRONMENT:
The physical and mental requirements along with the work environment characteristics described here are representative of those an individual encounters while performing the essential functions of this position.
Office: While performing the essential functions of this job the employee is frequently required to reach, grasp, stand and/or sit for long periods of time (up to 90% of the shift), walk, talk and hear; occasionally required to lift and/or move up to 25 pounds. The noise level in the work environment is usually moderately quiet, with frequent interruptions and multiple demands.
Additional Information OUR CULTURAL BELIEFS:
Patient Minded I act with the patient’s best interest in mind.
Client Delight I own every client experience and its impact on results.
Take Action I am empowered and hold myself accountable.
Embrace Diversity I create an environment of awareness and respect.
Grow Talent I own my development and invest in the development of others.
Win Together I passionately connect with anyone, anywhere, anytime to achieve results.
Communication Matters I speak up to create transparent, thoughtful, and timely dialogue.
Always Innovate I am bold and creative in everything I do.
All your information will be kept confidential according to EEO guidelines.
Our team is aware of recent fraudulent job offers in the market, misrepresenting EVERSANA. Recruitment fraud is a sophisticated scam commonly perpetrated through online services using fake websites, unsolicited e-mails, or even text messages claiming to be a legitimate company. Some of these scams request personal information and even payment for training or job application fees. Please know EVERSANA would never require personal information nor payment of any kind during the employment process. We respect the personal rights of all candidates looking to explore careers at EVERSANA.
EVERSANA is committed to providing competitive salaries and benefits for all employees. If this job posting includes a base salary range, it represents the low and high end of the salary range for this position and is not applicable to locations outside of the U.S. Compensation will be determined based on relevant experience, other job-related qualifications/skills, and geographic location (to account for comparative cost of living). This role is eligible for hire in select U.S. locations based on business and operational considerations. For a full list of locations, visit eversana.com/careers. More information about EVERSANA’s benefits package can be found at eversana.com/careers. EVERSANA reserves the right to modify this base salary range and benefits at any time.
From EVERSANA’s inception, Diversity, Equity & Inclusion have always been key to our success. We are an Equal Opportunity Employer, and our employees are people with different strengths, experiences, and backgrounds who share a passion for improving the lives of patients and leading innovation within the healthcare industry. Diversity not only includes race and gender identity, but also age, disability status, veteran status, sexual orientation, religion, and many other parts of one’s identity. All of our employees’ points of view are key to our success, and inclusion is everyone's responsibility.
Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for EVERSANA. The policy regarding requests for reasonable accommodations applies to all aspects of the hiring process. If reasonable accommodation is needed to participate in the interview and hiring process, please contact us at [email protected].
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Salary Context
This $103K-$124K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At EVERSANA, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($113K) sits 46% below the category median. Disclosed range: $103K to $124K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EVERSANA AI Hiring
EVERSANA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Overland Park, KS, Overland Park, KS, US, New York, NY. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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