Interested in this AI/ML Engineer role at AstraZeneca?
Apply Now →Skills & Technologies
About This Role
Location Gaithersburg, Maryland, United States Job ID R-261022 Date posted 30/09/2026
WHY JOIN US?Evinova is a health-tech business focused on accelerating better health outcomes by advancing digital transformation across the life sciences sector. By combining science-based expertise, evidence-led rigor, and deep human insight, we design digital solutions that enable healthcare to work better for everyone.
Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision-making, and ultimately delivering better outcomes for patients worldwide.
At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast-paced, high-impact environment.
We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in-person teamwork, innovation, and meaningful connection.
This role is located in Gaithersburg, Maryland and follows a hybrid work model. Candidates must reside within commuting distance of Gaithersburg or be willing to relocate for this opportunity.
Introduction to Role
The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is the cloud platform engineering team responsible for building and operating the infrastructure that enables our AI Engineers and Data Scientists to deploy Generative AI applications reliably, securely, efficiently and at scale.
As a Senior Cloud Platform Engineer on the ML/AI Ops team, you will design, build and operate the AWS platform that runs our production Generative AI, agentic AI and conversational AI workloads. Most of the code you write will be AWS CDK (TypeScript and/or Python) that creates the infrastructure for other teams to build on.
This is a cloud and platform engineering role rather than an AI application development role. You will partner closely with the engineers and Data Scientists who build agents and models, and provide the infrastructure, deployment patterns, model access, observability and operational capabilities they need to move solutions from experimentation into reliable production environments.
You will work across AWS infrastructure, infrastructure as code (IaC), Amazon Bedrock AgentCore, Amazon ECS, CI/CD, SageMaker Unified Studio, AI gateways, observability, scalability, reliability, security, governance and cost optimization. Your work will establish reusable platform capabilities that allow teams across Evinova to deploy and operate solutions faster and more reliably while meeting the requirements of a highly regulated pharmaceutical environment.
Accountabilities
Cloud Platform Engineering
- Design, build and operate scalable AWS cloud platform capabilities for production ML/AI and Generative AI workloads.
- Create reusable infrastructure, tooling and deployment patterns that enable AI Engineers and Data Scientists to independently deploy and operate their applications.
- Write and maintain AWS IaC primarily AWS CDK in TypeScript and/or Python, including reusable CDK constructs that other teams consume.
- Build and operate containerized workloads using Amazon ECS and AgentCore.
- Develop reusable platform capabilities across compute, networking, IAM, secrets management, storage, model access and workload isolation.
- Build and maintain CI/CD and GitOps workflows that enable safe, automated and repeatable deployments across environments.
- Partner with engineers and Data Scientists to transition prototypes and research workloads into resilient, production-grade services.
- Build self-service capabilities and automation that improve developer experience and reduce operational toil.
Reliability, Scalability & Operational Excellence
- Engineer platform capabilities that improve the availability, scalability, resiliency and performance of production GenAI workloads.
- Design and implement autoscaling, load balancing, retries, timeouts, fallback, rate limiting and failure-recovery strategies.
- Establish monitoring, alerting, SLOs, runbooks and production-readiness standards for ML/AI workloads.
- Troubleshoot complex production issues across AWS infrastructure, container, application and model-provider layers.
- Automate operational processes and proactively identify opportunities to improve platform reliability and performance.
- Drive cloud and model cost optimization through capacity management, workload optimization and data-driven analysis.
AI Gateway & Model Access
- Build and operate a centralized AI gateway that routes requests across model providers.
- Provide secure, reliable and governed access to foundation models through Amazon Bedrock, OpenAI, Anthropic, Microsoft Foundry, Gemini Enterprise Agent Platform (formerly Vertex AI) and other model platforms.
- Implement model/provider routing, fallback, authentication, rate limiting, quotas and cost controls.
- Enable AI teams to evaluate and change model providers without tightly coupling their applications to individual model endpoints.
- Provide the compute, networking, storage and runtime infrastructure to reliably operate agentic AI, RAG and conversational AI workloads in production.
Observability, Governance & Cost Optimization
- Build platform-level observability for GenAI workloads, including token consumption, latency, throughput, errors, model/provider performance and cost attribution by team and application.
- Implement standardized tracing, logging, metrics and alerting capabilities that can be adopted across AI applications.
- Integrate observability technologies such as Amazon CloudWatch, OpenTelemetry, Datadog and Splunk.
- Build the telemetry and data pipelines that AI teams use to evaluate and monitor production LLM behavior.
- Build appropriate security, auditability and governance controls for AI workloads operating within a regulated environment.
- Support compliance with applicable industry standards and practices, including Good Clinical Practice and Good Machine Learning Practice and other GxP related standards and practices.
Representative Projects
- Build a library of AWS CDK constructs that gives an AI team a production-ready Amazon ECS service, with IAM, secrets, networking and observability, from a single import.
- Deploy and operate a centralized AI gateway on Amazon ECS, with multi-provider routing, fallback, quotas and rate limiting.
- Build multi-account CI/CD with CDK Pipelines or GitOps, with safe, staged deployments across environments.
- Implement token cost attribution by team and application with SLO and burn-rate alerts.
- Migrate container workloads from Amazon EKS to Amazon ECS without loss of reliability.
Essential Skills/Experience
- Minimum of 4 years of hands-on experience in a platform engineering, infrastructure, site reliability engineering (SRE) or DevOps role where infrastructure code was your main output.
- Deep hands-on AWS cloud engineering experience, including designing, deploying and operating production cloud-native infrastructure.
- Strong experience with AWS services such as Bedrock, ECS, IAM, VPC, load balancing, S3, CloudWatch, Secrets Manager and related services.
- Strong hands-on experience with IaC. AWS CDK using Python and/or TypeScript is preferred. Strong Terraform engineers who are willing to work in CDK are welcome.
- Strong experience with Docker and Amazon ECS, including workload deployment, autoscaling, resource management, health checks, networking, security and production troubleshooting.
- Experience designing and maintaining CI/CD and/or GitOps pipelines for production cloud workloads.
- Strong understanding of cloud and platform engineering principles, including high availability, scalability, resiliency, observability, performance, security and cost optimization.
- Experience with on-call rotations, incident response and root-cause analysis for production systems.
- Strong software engineering skills in Python and/or TypeScript, with experience building production-quality platform tooling and automation.
- Experience implementing production observability using technologies such as CloudWatch, OpenTelemetry, Prometheus, Splunk, Datadog, Pydantic Logfire, Langfuse or comparable tools.
- Demonstrated experience partnering with software, data or ML/AI teams to transition workloads from experimentation into reliable production environments.
- Proven ability to collaborate effectively across ML/AI engineering, software engineering, security, infrastructure and product teams.
- Strong written and verbal communication skills, including the ability to clearly document infrastructure architecture, operational processes and platform standards.
Preferred Skills/Experience
- Experience building internal developer platforms or self-service cloud capabilities used by multiple engineering teams.
- Experience supporting Generative AI/LLM workloads in production, with an understanding of their unique operational challenges including model availability, latency, token consumption, evaluation and cost.
- Hands-on experience with Amazon Bedrock or another enterprise foundation-model platform.
- Experience implementing and/or configuring AI/model gateways, proxies or routing layers, including model routing, fallback, rate limiting and provider abstraction.
- Experience operating infrastructure supporting agentic AI, multi-agent systems, RAG pipelines or conversational AI.
- Experience with AgentCore or comparable managed agent-runtime capabilities.
- Experience supporting Model Context Protocol (MCP) tools, servers or services.
- Experience with LLM evaluation and observability platforms such as Arize Phoenix, Langfuse, Braintrust, Freeplay or Logfire.
- Experience supporting production ML/AI infrastructure within pharmaceutical, healthcare, financial services or another highly regulated industry.
- Understanding of GxP and/or other controls applicable to regulated ML/AI systems.
- Experience with Kubernetes (Amazon EKS), including migrating container workloads to Amazon ECS.
This Role Is Not a Good Fit If
- Your main experience is building agents, RAG pipelines, prompts or LLM applications, and you have not owned production infrastructure.
- You have not written or maintained infrastructure as code for production systems.
- You prefer research or model development to operating production platforms.
Personal Attributes
- Platform-minded: You think beyond individual applications and build reusable capabilities that enable multiple engineering teams.
- Operationally focused: You care about reliability, scalability, observability, security, performance and what happens after a workload reaches production.
- Hands-on: You are comfortable writing code, building infrastructure, operating container platforms and troubleshooting production systems.
- Customer-obsessed: You view the AI Engineers and Data Scientists using the platform as your customers and continually look for ways to improve their developer experience.
- Organized and attentive to detail: You effectively manage multiple initiatives and competing priorities.
- Collaborative and inclusive: You foster a positive engineering culture where teams share knowledge and solve problems together.
- Comfortable working autonomously: You like working in an evolving environment where you can help establish new systems, patterns, and best practices.
- Curious and committed to staying current: You love learning and applying that knowledge to the systems you build. You stay current with the latest AI trends and technologies and apply that knowledge to your work pragmatically.
SO, WHAT’S NEXT?
To be considered for this exciting opportunity, please complete the full application on our website at your earliest convenience – it is the only way that our Recruiter and Hiring Manager can know that you feel well qualified for this opportunity. If you know someone who would be a great fit, please share this posting with them.
Where can I find out more?
- Explore what we’re building: www.evinova.com
- Stay connected and see our impact in action: https://www.linkedin.com/company/evinova/
Apply today to bring smarter, faster clinical trials to life!
Evinova is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. Evinova is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing [email protected].
#LI-Hybrid
The annual base pay for this position ranges from $145,000 - $185,000 USD. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience.
In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program. Benefits offered included a qualified retirement program [401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans.
Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
Date Posted
01-Oct-2026 Closing Date
22-Oct-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 $145K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $145K to $185K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
AstraZeneca AI Hiring
AstraZeneca has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Gaithersburg, MD, US, Cambridge, MA, US, London, England, UK. Compensation range: $108K - $214K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.