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About This Role
Location Gaithersburg, Maryland, United States Job ID R\-253422 Date posted 31/05/2026
Introduction to role:
Are you ready to design the enterprise AI backbone that powers faster science and smarter operations? In this senior leadership role, you will define and scale platform and enabling architectures that turn agentic AI, foundation models, and interoperable context management into measurable outcomes—accelerating decisions in R\&D and across the enterprise while meeting the highest standards of data governance and regulatory compliance.
You will develop the roadmap and reference architectures applicable to open\-source and also proprietary ecosystems. You will collaborate with teams using Amazon Q, Amazon Bedrock, SageMaker, OpenAI, Databricks, and other new technologies. Your work will connect the dots across domains, simplify a complex landscape, and increase the agility of critical business processes—driving efficiencies, reducing risk, and unlocking value at scale.
Can you translate brand new AI into secure, scalable platforms that speed delivery of life\-changing medicines while improving the way we operate every day? If you thrive on uniting diverse experts to tackle complex challenges and make decisive progress, this is your opportunity to lead.
Accountabilities:
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- Enterprise AI Strategy: Define and evolve the organizational AI architecture strategy aligned with scientific and business objectives, ensuring platforms and products deliver tangible value.
- Agentic AI and Large\-scale Models: Lead architecture build for multi\-agent systems and foundational AI models (LLMs, multimodal), enabling resilient, observable, and governable solutions.
- Reference Frameworks and Protocols: Establish reusable blueprints and standards for platforms across both open\-access and licensed environments to accelerate safe adoption and scale.
- Interoperability via MCP: Drive adoption of the Model Context Protocol for consistent context management and interoperability across tools and services.
- Scalable Platforms on Cloud and Mixed Environments: Develop cloud and hybrid environment AI platforms with AWS, OpenAI, Databricks, and related services to improve enterprise throughput, reliability, and cost efficiency.
- AI Lifecycle Enablement: Enable the full AI lifecycle—from discovery and MVP to productionization, optimization, and retirement—backed by clear SLOs and feedback loops.
- Advanced AI Capabilities: Design and guide implementation of RAG patterns, vector databases, knowledge systems, and the data pipelines they depend on.
- Responsible and Secure AI: Embed governance, compliance, and risk management, anticipating threats such as data poisoning, model theft, and adversarial attacks, and translating regulations into actionable controls.
- Cross\-Enterprise Alignment: Partner with product, data governance, security, engineering, and business leaders to align AI initiatives and accelerate high\-value use cases.
- Leadership and Mentorship: Build and mentor high\-performing enterprise and solution architecture teams, developing skills, career paths, and delivery excellence.
- Value Acceleration: Identify, assess, and prioritize use cases with business stakeholders; translate strategy into practical solutions and constructively challenge low\-value or misaligned initiatives.
- Business\-Driven Delivery: Gather insights from users, data scientists, engineers, and operations to align delivery with current and future needs, turning them into scalable, reliable processes.
- Technology Selection and Integration: Select fit\-for\-purpose technologies across open\-source and commercial platforms, recommending cloud, on\-premises, or hybrid deployment models and ensuring seamless integration with data and analytics ecosystems.
- Continuous Improvement and MLOps: Evaluate tools and practices across data, models, and software engineering; set up feedback mechanisms for service performance, model recalibration, and retraining.
- ML/AI Pipelines: Guide pipeline architecture decisions across data management, governance, model development, deployment, and production operations, with clear trade\-off reasoning.
- Modern Engineering: Apply strong software engineering and DevOps principles, including Git, containers, Kubernetes, and CI/CD, to increase speed and reliability.
- Applied Data Science Understanding: Work fluently with analytics and ML concepts and tooling (e.g., SAS, R, Python, TensorFlow, ensembles, neural networks) to bridge architecture and data science practices.
- Executive Thought Leadership: Act as a change agent and trusted advisor; communicate opportunities, limitations, and risks of AI to senior stakeholders and influence decision\-making.
- Enterprise Collaboration: Build strong partnerships across data science, engineering, architecture, and executive leadership to align around shared outcomes.
- Information Architecture Ownership: Deliver conceptual and logical models for operational, master, and data products; define information flows, master and reference data, and metadata to meet capability needs.
- Strategy\-Evolving IA: Work with business leaders to evolve information architecture in line with strategy and capability roadmaps.
- Domain and Transformation Leadership: Own AI designs for large or complex capability domains and take accountability for enterprise architecture across major transformation programs.
- Design Assurance and Alignment: Create enterprise architecture blueprints and review project designs to ensure alignment with target architectures and standards.
- Embedded Data Governance: Partner with Data Offices to embed governance with measurable controls (e.g., master data consumption, classification metadata) across designs and access processes.
- Patterns for Analytics and Operations: Select and define architectures and patterns for reporting, analytics, data science, digital, and operational use cases.
- Strategic and Tactical Support: Provide planning, design expertise, and delivery support across technology standards, models, and enterprise architecture considerations.
- Integration Architecture: Support or lead AI integration architecture and end\-to\-end data integration design for complex initiatives.
- IA Governance and Standards: Secure approval for IA artifacts and enforce standard enterprise data element names, abbreviations, characteristics, and domains throughout the lifecycle.
- Resource and Work Package Management: Define and manage work packages for internal and flexible resources, ensuring clarity of outcomes and accountability.
- Demand and Financials: Manage demand planning and recharge activities for AI and technology programs in partnership with practice leaders and project managers.
Essential Skills/Experience:
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- Bachelor’s degree, or equivalent experience, in data science, AI engineering, or a related field.
- A seasoned professional with 15\+ years of experience, anchored by extensive leadership in AI platform strategy. Consistently transforms theoretical architectural models into robust, real\-world platforms.
- Experience with conceptual and logical data modelling techniques and tools.
- Experience defining and applying information and data governance standards in regulated environments.
- Brings a strong mix of data and information architecture, analysis, and engineering expertise.
- Experience with established IT architecture patterns, methodologies, and AI platforms such as Amazon Bedrock, Amazon Q, SageMaker, Azure AI, and Google Cloud AI.
- Strong understanding of AI platform concepts and cloud\-based containerization strategies for hybrid environments.
- Able to select the right AI architecture and technologies based on business use cases, with a strong understanding of the full AI lifecycle.
- Lead a small team of AI architects and help shape AI strategy and direction across the enterprise landscape.
Desirable Skills/Experience:
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- Postgraduate degree in MIS, AI, data science, or a related field.
- Ability to lead a small team of AI enterprise architects while guiding and supporting multiple projects simultaneously.
- Recognized thought leader in applying AI within the enterprise and across the industry.
- Extensive experience in senior AI, data science, data engineering, and AI architecture roles, with a strong track record of designing and delivering end\-to\-end and point architecture blueprints for large\-scale, real\-world use cases.
- Experience applying AI and data governance frameworks within a commercial organization.
- Experience working in Agile AI delivery and definition scrums.
- Experience using tools such as metadata cataloguing, data modelling, and enterprise architecture platforms.
- Hands\-on experience building AI models, including LLMs and LVMs, working across diverse data types to deliver accurate and reliable outcomes.
- Experience working in the pharmaceutical AI industry.
- Experience in AI architecture, pipeline planning, and deploying production workflows for AI models.
- Ability to simplify the enterprise landscape and reduce technology debt.
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life\-changing medicines. In\-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.
Why AstraZeneca
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We are AstraZeneca’s Enterprise AI Unit—a global community pioneering how AI transforms healthcare. We are building an AI\-empowered organisation from the ground up, uniting scientists, leaders, strategists, and technologists with a shared drive to apply AI where it matters most. Here, your architecture will not sit on a shelf gathering dust. It will empower clinicians and colleagues to make fast, informed decisions that ultimately help patients. You will have the autonomy to set enterprise patterns and see your solutions adopted at scale within a collaborative culture that values kindness alongside ambition.
Employee Benefits
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We believe that to do your best work, you need to feel your best. The annual base pay for this position ranges from $190,956\.80 to $286,435\.20. You will be eligible for various incentives, including short\-term incentive bonuses and equity\-based awards for salaried roles. Our comprehensive package offers premium private medical insurance, dental and vision coverage, and a generous company pension scheme. You will also benefit from dedicated funding for continuous learning, paid time off, and flexible working arrangements to help you thrive.
How We Work
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When we bring together teams with varied strengths in one space, we unlock daring ideas with the ability to encourage life\-changing medicines. In\-person working builds the space required to engage, move quickly, and reshape perspectives. That is why we typically spend at least three times weekly working from the office. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and bold world.
Call to Action:
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Lead the architecture that transforms breakthrough AI into enterprise\-wide value—step in and make your impact count.
If you are ready to lead enterprise\-scale AI that delivers measurable business value and meaningful patient impact, step forward and show us how you will make it real. Apply today.
\#EAI
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Date Posted
01\-Jun\-2026
Closing Date
18\-Jun\-2026
Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.
Salary Context
This $190K-$286K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AstraZeneca, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($238K) sits 32% above the category median. Disclosed range: $190K to $286K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AstraZeneca AI Hiring
AstraZeneca has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Wilmington, DE, US, Gaithersburg, MD, US. Compensation range: $204K - $319K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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