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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
The Senior AI Agency Engineer designs, builds, and deploys enterprise\-grade AI solutions that connect generative AI, workflow automation, compliance controls, and business systems to accelerate pharmaceutical and healthcare marketing operations.
This role combines the responsibilities of a senior software engineer, solutions architect, and client\-facing technical consultant. The individual leads technical discovery with clients, translates business requirements into scalable technical solutions, develops integrations and AI workflows, and partners across product, engineering, data, security, and delivery teams to bring production\-ready AI solutions to market.
The ideal candidate possesses deep expertise in modern software engineering, cloud\-native architecture, enterprise integrations, and generative AI technologies, along with the communication skills needed to engage both technical teams and executive stakeholders.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:
Client Discovery \& Technical Leadership
- Lead technical discovery sessions with client business, IT, architecture, security, data, and operations teams.
- Translate business objectives, workflows, and operational challenges into technical requirements, architecture designs, and implementation plans.
- Assess enterprise application landscapes, integration requirements, data environments, security constraints, and operating models.
- Serve as a trusted technical advisor to clients and internal stakeholders.
- Communicate complex AI, data, platform, and integration concepts to both technical and executive audiences.
- Identify technical risks, dependencies, assumptions, and mitigation strategies early in engagements.
- Develop technical prototypes and proofs of concept to validate solution feasibility and accelerate decision\-making.
Solution Architecture \& Engineering
- Design end\-to\-end solutions that connect AI Agency capabilities with client systems, data sources, content repositories, workflows, and approval environments.
- Build and configure production\-quality integrations using APIs, webhooks, event\-based patterns, secure file exchanges, connector frameworks, and enterprise integration platforms.
- Develop client\-specific configuration and deployment assets while preserving the integrity of the shared platform architecture.
- Contribute hands\-on code across backend services, integration components, data pipelines, AI workflows, and supporting user experiences.
- Design and implement AI\-enabled workflows using large language models, retrieval\-augmented generation, agent orchestration, structured outputs, evaluation frameworks, and human approval checkpoints.
- Implement authentication, authorization, identity federation, secrets management, data mapping, error handling, observability, and audit requirements.
- Create technical prototypes when needed to validate feasibility, reduce ambiguity, or accelerate client decision\-making.
- Support the transition from prototype to stable, supportable production deployment.
Enterprise Integrations \& Data Engineering
- Design, build, and maintain integrations using APIs, webhooks, event\-driven architectures, secure file exchanges, and enterprise integration platforms.
- Develop system connectors to enterprise platforms including:
+ Veeva Vault / PromoMats
+ Salesforce Marketing Cloud
+ SharePoint
+ Adobe Experience Cloud
+ Snowflake
+ BigQuery
+ Vertex AI
- Build and maintain data pipelines supporting reporting, analytics, and AI workflows.
- Design data mapping, transformation, validation, and synchronization processes.
- Partner with data engineering teams to support ETL/ELT and analytics initiatives.
Cloud Infrastructure, Deployment \& Operations
- Develop cloud\-native solutions with a preference for Google Cloud Platform.
- Implement CI/CD pipelines and automated deployment processes.
- Build and maintain containerized applications using Docker and Kubernetes.
- Ensure application reliability through monitoring, logging, alerting, and observability practices.
- Maintain high standards of testing, code quality, scalability, and operational excellence.
- Support production troubleshooting, performance optimization, and incident resolution.
- Modeling inclusive behaviors and proactively managing bias.
- All other duties as assigned*.*
Qualifications
The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
- 9\+ years of experience in software engineering, solution engineering, technical consulting, platform implementation, or related engineering roles.
- 5\+ years designing and deploying production cloud\-based AI, data, or enterprise application solutions.
- Advanced programming skills in Python and at least one additional modern language such as JavaScript/TypeScript, C\#, Java, or .NET.
- Significant experience designing:
+ APIs
+ Data pipelines
+ Event\-driven architectures
+ Enterprise integrations
+ Authentication and security frameworks
+ Distributed systems
- Hands\-on experience with generative AI technologies including:
+ LLMs
+ Prompt engineering
+ RAG
+ Embeddings
+ Vector databases
+ Agent orchestration
+ AI evaluations
+ Observability and guardrails
- Experience with Git, CI/CD, automated testing, containers, and cloud\-native development practices.
- Strong communication, consulting, and stakeholder management skills with the ability to influence
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 $122K-$207K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($164K) sits 23% below the category median. Disclosed range: $122K to $207K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EVERSANA AI Hiring
EVERSANA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Overland Park, KS, US, Chicago, IL, US. Compensation range: $207K - $213K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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