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About This Role
Senior Director, AI, Data, and Architecture
Position Summary
The Senior Director, AI, Data, and Architecture is responsible for leading EnlivenHealth’s AI strategy, data platform direction, enterprise architecture, and technical modernization across the Product and Engineering organization.
This role serves as a senior technical leader responsible for helping EnlivenHealth responsibly apply AI to improve product capabilities, engineering productivity, analytics, automation, and customer outcomes. The Director will define practical AI patterns, data foundations, architecture standards, and modernization roadmaps that enable scalable, secure, reliable, and innovative healthcare technology solutions.
The Senior Director partners closely with Product, Engineering, Platform, Security, Cloud Operations, Quality, and business stakeholders to translate business priorities into executable AI, data, integration, and architecture plans. This role requires strong technical judgment, healthcare technology experience, and the ability to influence across teams without relying solely on direct authority.
Key Responsibilities
AI Strategy and Enablement
- Define and lead EnlivenHealth’s AI strategy across Product and Engineering.
- Identify, prioritize, and guide AI use cases that improve patient engagement, clinical workflows, financial operations, analytics, automation, and internal productivity.
- Establish AI architecture patterns, development standards, lifecycle practices, evaluation methods, and guardrails for responsible AI adoption.
- Partner with Product leaders to develop customer\-facing AI capabilities that create measurable value while respecting healthcare privacy, security, explainability, and customer trust requirements.
- Partner with Engineering and Quality teams to expand AI\-assisted software development, test automation, documentation, code review, knowledge management, and operational workflows.
- Track AI adoption, productivity impact, quality impact, risks, and lessons learned.
- Ensure AI initiatives align with Omnicell and EnlivenHealth policies, customer commitments, data\-use obligations, and applicable regulatory expectations.
Data Platform Strategy and Governance
- Define the strategic direction for EnlivenHealth’s data platform, including data architecture, data flows, data quality, data governance, analytics enablement, and AI readiness.
- Support the development of a broader patient, pharmacy, financial, clinical, and outcomes data foundation.
- Establish practical standards for data ingestion, transformation, storage, lineage, access, quality, retention, and security.
- Partner with Security, Privacy, Legal, Compliance, Product, and Engineering stakeholders to ensure healthcare data is handled appropriately.
- Identify opportunities to consolidate data assets, reduce duplication, and create trusted data products.
Enterprise and Solution Architecture
- Define and maintain enterprise architecture principles, standards, decision practices, and reference patterns for EnlivenHealth platforms.
- Lead solution architecture for major product initiatives, platform modernization, integrations, AI\-enabled capabilities, and data\-driven services.
- Partner with Product and Engineering leaders to ensure architecture decisions align with customer needs, scalability, security, reliability, interoperability, and operating efficiency.
- Identify technical debt, architectural risk, platform fragmentation, and scalability constraints; develop practical plans to address them.
Modernization, API, and Integration Architecture
- Own and maintain the technical modernization roadmap in partnership with Product, Engineering, Platform, Security, and Cloud Operations.
- Drive patterns that simplify platforms, reduce operational risk, improve resiliency, and enable faster product delivery.
- Define API and integration architecture standards, including authentication, authorization, versioning, monitoring, documentation, and lifecycle management.
- Support scalable integration with pharmacy management systems, health systems, payers, partners, and other healthcare ecosystem participants.
Required Qualifications \& Skills
- 10\+ years of progressive experience in software engineering, enterprise architecture, solution architecture, data architecture, cloud architecture, AI enablement, or related technology leadership roles.
- 5\+ years leading architecture, technical strategy, data platform, AI, or modernization initiatives across complex SaaS or enterprise software environments.
- Practical experience with AI technologies and implementation patterns, including generative AI, machine learning concepts, responsible AI practices, model evaluation, prompt/workflow design, and AI\-enabled software delivery.
- Experience defining AI standards, architecture roadmaps, technical patterns, integration approaches, and modernization strategies.
- Strong understanding of cloud\-native architecture, distributed systems, APIs, data platforms, security patterns, observability, resiliency, and scalable SaaS operations.
- Experience with data architecture, data governance, analytics enablement, and modern data platform concepts.
- Strong knowledge of healthcare data privacy, security, compliance, and customer trust considerations, including HIPAA, SOC 2, HITRUST, or similar control environments.
- Excellent communication skills with the ability to explain complex technical tradeoffs to technical, executive, customer, and non\-technical audiences.
- Proven ability to balance innovation, execution, security, operational reliability, and long\-term platform health.
Preferred Qualifications
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field.
- Master’s degree or MBA with a technology, data, AI, or healthcare focus.
- Experience in healthcare technology, pharmacy technology, patient engagement, clinical workflow, claims, or financial technology.
- Experience with AWS cloud services, AI platforms, event\-driven architecture, containerized platforms, serverless technologies, and cloud\-native modernization.
- Experience with AI platforms and services such as AWS Bedrock, Kiro, OpenAI, Anthropic Claude, GitHub Copilot, or comparable technologies.
- Experience establishing AI governance, responsible AI review processes, model evaluation practices, and secure AI development patterns.
EEO, Privacy, and Adaptability
Omnicell welcomes applications from all individuals, valuing a wide range of perspectives and backgrounds. As an equal opportunity employer, we do not discriminate based on race, gender, religion, sexual orientation, gender identity, national origin, veteran status, or disability. We are committed to making our recruitment process accessible to everyone. We offer support and reasonable adjustments for individuals with disabilities during our hiring process. If you need assistance, please contact us at [email protected].
At Omnicell, respect for privacy and confidentiality is paramount. We adhere to strict policies to prevent discrimination or retaliation against those who engage in open conversations about compensation. However, employees privy to compensation information as part of their job role are expected to maintain confidentiality, except in specific circumstances outlined by law, such as during formal complaints, investigations, or as required by legal obligations. Learn more about our privacy practices: https://www.omnicell.com/privacy/
Please note that Omnicell reserves the right to modify job roles and responsibilities as needed to meet our organization's evolving needs and drive our mission forward.
About Us
At Omnicell, innovation starts with people who are passionate about making healthcare safer and smarter. Since 1992, we’ve been transforming the future of pharmacy care through bold ideas and hands\-on solutions that make a real impact on clinicians and patients’ lives.
We build outcomes\-driven technology—from robotics to intelligent software—that helps clinicians work more efficiently and ensures patients get the care they need. Every improvement, every breakthrough, every idea is rooted in our belief that better is always possible.
But what sets us apart isn’t just the work we do, it’s how we do it. Our Culture of Care shapes everything, from how we show up for each other to how we solve tough problems together. You’ll find a team that has your back, leaders who listen, and a shared commitment to building something that matters.
Here, careers are more than job titles, they are journeys of purpose and possibility. Whether you’re just getting started or ready to grow in new directions, we’ll meet you where you are, with support, flexibility, and opportunity that matches your ambition.
If you’re driven by purpose and ready to shape what’s next in healthcare, there’s a place for you at Omnicell.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Omnicell, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Omnicell AI Hiring
Omnicell has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Worth, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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