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About This Role
Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life\-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.
Position Overview
The Sr. AI Integration Analyst sits within the Automation Business Solutions pillar of the AI Strategy organization. This role is focused on designing and building the integration layer that connects enterprise systems to AI solutions, directly enabling use case delivery across the GenAI and MLOps pillars. The Sr. Integration Engineer works closely with the Automation Solutions Architect and cross\-functional AI Strategy teams, partnering with the IT Integrations team on enterprise standards while owning the AI\-specific integration work.
Tools commonly used in this role include enterprise middleware platforms (MuleSoft, Dell Boomi, or Azure Integration Services), REST and FHIR APIs, and Python. This position may include on\-call responsibilities as part of a production support rotation.
Essential Duties
Include, but are not limited to, the following:
- Designs, develops, and maintains integrations between enterprise platforms and AI solutions in support of the AI Strategy organization.
- Builds and maintains data pipelines that expose structured enterprise data for AI model development, training, and deployment.
- Partners with GenAI and MLOps teams to understand and deliver on integration requirements for active AI use cases.
- Collaborates with the IT Integrations team to ensure AI integration work aligns with enterprise standards and infrastructure.
- Manages the full integration development lifecycle including design, development, testing, change control, go\-live, and support.
- Authors technical specifications and integration documentation for current and future AI use cases.
- Develops and promotes integration best practices within the AI Strategy organization.
- Writes and executes test plans to validate functional, quality, and security requirements.
- Monitors live integrations and drives resolution of issues through durable, well\-documented solutions.
- Communicates effectively with technical and non\-technical stakeholders across the organization.
- Upholds the company mission and values through accountability, innovation, integrity, quality, and teamwork.
- Supports and complies with the company’s Quality Management System policies and procedures.
- Maintains regular and reliable attendance.
- Ability to act with an inclusion mindset and model these behaviors for the organization.
Minimum Qualifications
- Bachelor’s degree in engineering, physics, computer science, math, information systems, statistics, or related field or Associates degree and 2\+ years of related experience or High School/GED and 4\+ years of experience.
- 5\+ years of IT experience supporting system integrations.
- 3\+ years of Interface Engine Development.
- Epic Bridges Certification.
- Ability to develop in Corepoint or another interface engine.
- Strong analytical, technical, and troubleshooting skills.
- Ability to analyze data, solve problems, and clearly communicate solutions.
- Ability to understand organizational requirements and their impact on the technical aspects of a solution.
- Ability to work in a complex multi‐communication‐channel environment.
- Skill at tracking tasks, defining next steps, identifying owners, and holding people accountable.
- Ability to coordinate activities involving multiple players, including vendors, subject matter experts, interface engine developers, and end users, to design, configure, and test interfaces.
- Ability to parse HL7 v2, XML, CSV, and HTML.
- Demonstrated ability to perform the essential duties of the position with or without accommodation.
- Authorization to work in the United States without sponsorship.
Preferred Qualifications
- Proficiency with REST and/or SOAP APIs and enterprise integration patterns.
- Hands\-on experience with at least one integration middleware platform.
- Strong backend development skills in Python, Java, or C\#.
- Experience designing and building ETL/ELT pipelines and working with structured data at scale.
- 2\+ years of hands\-on Epic integration experience (FHIR R4, Interconnect, or App Orchard).
- Familiarity with Epic Cogito analytics environment or Epic Clarity/Caboodle data models.
- Experience with HL7/FHIR standards and healthcare data models.
- Prior experience working within or directly supporting a GenAI, MLOps, or AI product team.
- Exposure to LLM APIs, prompt engineering, or agentic workflow concepts.
- Familiarity with cloud data platforms including Azure, AWS, or Snowflake.
- Knowledge of additional ERP platforms such as SAP, Oracle, or Workday.
The base pay for this position is $78,000\.00 – $156,000\.00\. In specific locations, the pay range may vary from the range posted.
Salary Context
This $78K-$156K range is in the lower quartile 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 Abbott, 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 ($117K) sits 46% below the category median. Disclosed range: $78K to $156K.
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.
Abbott AI Hiring
Abbott has 4 open AI roles right now. They're hiring across Research Scientist, AI Architect, AI/ML Engineer. Positions span La Jolla, CA, US, Waukegan, IL, US, Madison, WI, US. Compensation range: $156K - $298K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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