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About This Role
Company Description
EVERSANA INTOUCH® is a global, full-service marketing agency network serving the life sciences industry, and is the first – and only – agency network to be part of a fully integrated commercialization platform through EVERSANA®. We provide next-generation creative and media services, enterprise solutions and data analytics services for clients.
We get fired up when people talk about getting—and staying—healthy. That’s where we find our inspiration: in the very human experiences of patients, doctors, and even each other. Then, we collaborate on ways to make caring for one’s health more achievable, connecting patients and physicians with the information and tools they need.
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.
Job Description
We are seeking a Digital Asset Management (DAM) Solutions Architect to lead the design, configuration, integration, and evolution of digital asset management capabilities supporting modern content operations and AI-enabled content workflows.
This role sits at the intersection of technology architecture, content operations, information architecture, and digital publishing. The ideal candidate combines hands-on knowledge of enterprise DAM platforms with a strong understanding of taxonomy, metadata, digital rights, content lifecycle management, publishing requirements, and integration with upstream and downstream enterprise systems.
The DAM Solutions Architect will help translate business and content requirements into scalable DAM solutions—including metadata models, taxonomy structures, asset organization, workflow, governance, publishing patterns, and system integrations.
This role will also help establish DAM and content-management standards that enable content to be discoverable, reusable, interoperable, governed, and consumable by both human users and AI-enabled platforms and workflows.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:
DAM Architecture & Configuration
- Design and configure enterprise DAM solutions aligned to business, content, publishing, and technology requirements.
- Define asset structures, content models, folder and collection strategies, permissions, workflows, renditions, and lifecycle rules.
- Assess existing DAM implementations and identify opportunities to improve scalability, usability, governance, and interoperability.
- Translate business and content-management requirements into functional and technical DAM specifications.
- Establish patterns supporting modular content, source assets, derivatives, channel-specific renditions, and packaged digital deliverables.
- Support migration and onboarding of existing content and assets into DAM environments.
Taxonomy, Metadata & Information Architecture
- Design and maintain scalable metadata schemas, taxonomies, controlled vocabularies, and tagging strategies.
- Establish standards for descriptive, structural, operational, technical, compliance, attribution, and rights-management metadata.
- Define relationships between asset-level attributes, taxonomy values, keywords, tags, and other content classifications.
- Develop metadata strategies that improve search, discovery, reuse, automation, and reporting.
- Establish approaches for capturing AI-related content provenance and attribution, including generated or AI-modified content where applicable.
- Ensure metadata and taxonomy models can evolve as new brands, markets, channels, content types, and use cases are introduced.
Content Lifecycle, Governance & Digital Rights
- Define and document content and asset lifecycle processes from creation and approval through publication, reuse, expiration, archival, and retirement.
- Establish governance standards for metadata quality, taxonomy management, asset stewardship, and content hygiene.
- Design approaches for managing digital rights, including licensing, usage restrictions, geography, channel, talent, expiration, and other rights-related attributes.
- Partner with content operations and business stakeholders to define roles and responsibilities for ongoing DAM governance and asset stewardship.
- Help establish operating models for DAM administrators, content owners, librarians, and other asset-management roles.
Digital Publishing & Content Supply Chain
- Understand digital publishing requirements across web, email, social, media, CRM, and other digital channels.
- Define requirements for asset transformation, renditions, packaging, distribution, and downstream consumption.
- Design DAM capabilities supporting modular and omnichannel content operations.
- Ensure DAM architecture supports the movement of assets throughout the content supply chain—from creation and approval through publishing and activation.
- Partner with creative, content, marketing operations, and technology teams to establish scalable publishing patterns.
Systems Integration
- Design integration patterns between DAM platforms and upstream/downstream enterprise systems.
- Define APIs, events, webhooks, metadata mappings, authentication requirements, asset-transfer patterns, and integration contracts.
- Support integration with systems such as: + Content creation and design platforms + Content management systems (CMS) + Marketing workflow/project-management platforms + Medical/legal/regulatory review and approval platforms + Claims-management solutions + CRM and marketing automation platforms + Omnichannel orchestration and activation platforms + AI-enabled content-generation and content-management solutions
- Define appropriate integration triggers, audit requirements, exception handling, retry processing, and operational monitoring.
- Work closely with engineering teams to translate architectural designs into implementation-ready specifications.
AI-Enabled Content Operations
- Help design DAM capabilities that support AI-enabled content generation, retrieval, reuse, and automation.
- Establish metadata and content structures that enable assets to provide reliable context to AI applications and agents.
- Define approaches for distinguishing and governing human-created, AI-generated, and AI-modified content.
- Partner with AI/product teams to establish integration patterns between DAM platforms and AI-enabled content platforms.
- Identify opportunities to use AI to improve tagging, classification, metadata enrichment, asset discovery, and content operations while maintaining appropriate governance and human oversight.
Qualifications MINIMUM KNOWLEDGE, SKILLS AND ABILITIES:
The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.
- Significant experience designing, implementing, configuring, or architecting enterprise Digital Asset Management solutions.
- Strong understanding of DAM taxonomy, metadata architecture, controlled vocabularies, tagging, and information architecture.
- Experience with enterprise DAM platforms such as Adobe Experience Manager Assets / Adobe Assets, Aprimo, Bynder, Acquia DAM, Sitecore Content Hub, or comparable platforms.
- Strong understanding of digital content lifecycle management and content supply-chain processes.
- Experience designing integrations using REST APIs, events, webhooks, middleware, and related enterprise integration patterns.
- Understanding of digital publishing requirements across multiple channels and content formats.
- Experience with asset renditions, transformations, derivatives, packaging, and distribution.
- Working knowledge of digital rights management and content governance.
- Ability to translate business and operational requirements into scalable technical and information-architecture solutions.
- Experience collaborating across business, creative, content operations, product, engineering, and technology teams.
- Strong documentation and communication skills, including the ability to develop architecture diagrams, metadata models, integration specifications, and governance standards.
PREFERRED QUALIFICATIONS:
- Experience with Adobe Experience Manager Assets / Adobe Assets and the broader Adobe ecosystem.
- Experience integrating DAM with workflow, regulatory review, CRM, CMS, marketing automation, or omnichannel platforms.
- Experience with Veeva PromoMats, Veeva Vault, Adobe Workfront, Figma, Adobe Journey Optimizer, or comparable enterprise platforms.
- Experience within life sciences, pharmaceutical, or another highly regulated industry.
- Familiarity with medical/legal/regulatory (MLR) content-review processes.
- Experience supporting modular content and omnichannel content strategies.
- Familiarity with generative AI, semantic search, embeddings, knowledge retrieval, and AI-enabled content operations.
- Experience defining content provenance, attribution, or AI-related metadata standards.
- Experience working with cloud-native architectures and modern API-based integration patterns.
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 empower others to act now.
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.
Embrace Diversity I create an environment of awareness and respect.
Always Innovate I am bold and creative in everything I do.
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 $140K-$206K range is below 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 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 $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 ($173K) sits 18% below the category median. Disclosed range: $140K to $206K.
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.
EVERSANA AI Hiring
EVERSANA has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Kansas City, MO, US, Overland Park, KS, New York, NY. Compensation range: $206K - $206K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 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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