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About This Role
Posted Date 8/03/2026
Description
About Abbott
Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life\-changing technologies spans diagnostics, medical devices, nutrition, and branded generic medicines. With 115,000 colleagues serving people in more than 160 countries, Abbott is committed to advancing healthcare through innovation, data, and technology.
The Opportunity
Reporting to the Director of Information Management, Data \& Analytics, the Data \& AI Architect will play a critical role in defining and delivering Abbott's enterprise data and AI architecture vision. This leader will architect scalable, secure, and AI\-ready data platforms, enable advanced analytics and AI use cases, and establish the technical standards that support Abbott's transition to a modern data ecosystem.
The Data \& AI Architect will serve as the principal technical authority across data platforms, data products, analytics, integration patterns, and AI enablement capabilities. Working closely with Data Engineering, AI Engineering, Enterprise Architecture, Security, and business stakeholders, this role will ensure Abbott's data estate supports current operational needs while accelerating innovation in analytics, automation, and artificial intelligence.
What You'll Work On
Enterprise Data \& AI Architecture
- Define and maintain the enterprise data and AI reference architecture aligned with Abbott's Information Management and AI strategy.
- Develop future\-state architecture blueprints supporting Data Mesh, Data Fabric, cloud\-native analytics platforms, and AI\-enabled business capabilities.
- Establish architecture principles, standards, patterns, and governance frameworks for enterprise data and AI solutions.
- Partner with Enterprise Architecture to ensure alignment between business, application, data, and technology architectures.
- Evaluate emerging technologies, industry trends, and AI capabilities and translate them into practical adoption roadmaps.
Data Platform Architecture
- Design scalable and secure cloud\-native data platforms leveraging technologies such as Snowflake, Databricks, Microsoft Fabric, Azure Data Services, and related ecosystem tools.
- Define architecture standards for ingestion, transformation, storage, orchestration, observability, metadata management, and data sharing.
- Establish design patterns supporting structured, semi\-structured, streaming, and unstructured data workloads.
- Define architectural approaches for Infrastructure\-as\-Code, platform automation, containerization, and CI/CD enablement.
- Partner with platform and engineering teams to optimize scalability, resiliency, performance, and cost management.
Data Integration \& Data Products
- Define enterprise integration patterns supporting batch, near real\-time, API\-driven, and event\-driven architectures.
- Establish standards for data contracts, domain ownership, interoperability, and Data Product lifecycle management.
- Drive implementation of reusable data assets and federated Data Mesh principles across Abbott business domains.
- Ensure data products are discoverable, documented, governed, and aligned with business and AI consumption requirements.
- Partner with business units to translate strategic priorities into scalable information architectures.
AI \& Advanced Analytics Enablement
- Architect AI\-ready data ecosystems supporting machine learning, generative AI, predictive analytics, and agent\-based solutions.
- Design foundational capabilities including feature stores, vector databases, semantic layers, knowledge repositories, and Retrieval\-Augmented Generation (RAG) architectures.
- Establish architectural standards for model training data, feature engineering, metadata, lineage, and model operationalization.
- Collaborate with AI Engineering and Automation teams to ensure secure and scalable integration of AI services into enterprise workflows.
- Lead data readiness and AI architecture assessments for strategic AI investments and initiatives.
Cloud \& Digital Platform Strategy
- Partner with Cloud Infrastructure, Enterprise Architecture, and Security leaders to define and evolve Abbott's hybrid\-cloud and multi\-cloud strategy, ensuring alignment with enterprise technology, data, and AI objectives.
- Guide enterprise adoption of cloud\-native architecture patterns, Infrastructure as Code (IaC), DevOps, Platform Engineering, and automated operational practices that improve scalability, reliability, and delivery velocity.
- Drive technology simplification, platform rationalization, and modernization initiatives by identifying opportunities to consolidate legacy technologies, reduce technical debt, improve supportability, and optimize total cost of ownership.
- Ensure cloud platform architectures are designed to support modern analytics, AI, automation, and business\-critical workloads while maintaining resiliency, performance, security, and compliance standards.
Data Governance \& Information Management
- Partner with Governance and Information Management teams to establish metadata, lineage, data quality, and master data architecture standards.
- Ensure architectures support Abbott's regulatory, privacy, security, and compliance requirements.
- Define controls that enable trusted, auditable, and governed data across the enterprise.
- Support implementation of enterprise data catalogs, business glossaries, lineage solutions, and stewardship processes.
- Promote "trust by design" principles throughout the data and AI ecosystem.
Technology Leadership \& Delivery
- Provide architecture leadership for major strategic initiatives, ensuring alignment with enterprise standards and business outcomes.
- Conduct architecture reviews and approve solution designs for critical programs.
- Mentor engineers, architects, and technical teams in modern data management and AI engineering practices.
- Influence investment decisions, vendor evaluations, and platform roadmap development.
- Support program teams throughout the delivery lifecycle, ensuring technical quality, scalability, and long\-term maintainability.
Key Responsibilities
- Own enterprise\-wide data and AI architecture standards and roadmaps.
- Serve as lead architect for strategic data platform modernization initiatives.
- Drive architectural governance across analytics, integration, data products, and AI solutions.
- Define technology standards and reference implementations supporting cloud\-first architectures.
- Partner with Security, Infrastructure, Enterprise Architecture, and AI Engineering teams to ensure alignment and compliance.
- Lead proof\-of\-concepts and technology evaluations for new data and AI capabilities.
- Act as a trusted advisor to senior business and technology leadership.
Success Measures
Within the first 12\-24 months, the Data \& AI Architect will:
- Establish Abbott's target\-state Data \& AI Architecture and roadmap.
- Define enterprise standards for Data Products, AI\-ready datasets, integration, and governance.
- Accelerate modernization of legacy data environments while maintaining operational stability.
- Increase adoption of reusable, governed, and trusted data assets across the enterprise.
- Enable scalable AI and analytics capabilities that directly support business outcomes.
- Improve platform interoperability, data quality, and architectural consistency across Abbott's global landscape.
Required Qualifications
- Master's degree in Computer Science, Data Science, Information Systems, Engineering, or related field.
- 10\+ years of experience in enterprise data architecture, data engineering, analytics architecture, or cloud platform architecture.
- 5\+ years designing and implementing enterprise\-scale cloud data platforms.
- Demonstrated experience architecting modern data ecosystems using technologies such as Snowflake, Databricks, Microsoft Fabric, Azure Synapse, Azure Data Lake, or equivalent platforms.
- Experience designing architectures supporting AI/ML, Generative AI, RAG, feature stores, and advanced analytics workloads.
- Strong understanding of Data Mesh, Data Fabric, Data Products, and modern information management principles.
- Deep knowledge of enterprise integration patterns, APIs, event\-streaming platforms, and real\-time data architectures.
- Experience implementing data governance, metadata management, lineage, MDM, and data quality solutions.
- Strong understanding of regulatory and compliance considerations within healthcare, medical device, pharmaceutical, or highly regulated industries.
- Excellent stakeholder management, communication, and influencing skills.
Preferred Qualifications
- Experience within healthcare, life sciences, medical devices, pharmaceuticals, or regulated manufacturing industries.
- Hands\-on expertise with Snowflake, Databricks, Microsoft Fabric, Azure, Kubernetes, Apache Airflow, Delta Lake, Apache Iceberg, Kafka, or similar technologies.
- Experience with vector databases, semantic search platforms, knowledge graphs, and enterprise AI platforms.
- Relevant certifications in Azure, Cloud Architecture, Data Engineering, AI Engineering, or Enterprise Architecture.
- Experience supporting global organizations and large\-scale digital transformation programs.
The base pay for this position is $149,300\.00 – $298,700\.00\. In specific locations, the pay range may vary from the range posted.
Salary149,300\.00 \- 298,700\.00 Annual
Type
Full\-time
Salary Context
This $149K-$298K range is above the 75th percentile for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Information Technology Senior Management Forum, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($224K) sits 6% below the category median. Disclosed range: $149K to $298K.
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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager, AI Product Manager, Data Scientist. Positions span Basking Ridge, NJ, US, Fort Worth, TX, US, New York, NY, US. Compensation range: $154K - $413K.
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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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