AWS DevOps & AI Engineer

Job description

GCL: C2 Introduction to role Are you ready to scale secure, automated AWS platforms and bring GenAI into production to accelerate how life-changing medicines reach patients? Join a platform engineering group that partners across data, engineering, and AI/ML teams to deliver reusable cloud capabilities and operationalize AI applications where they matter most! You will build the foundations that let our scientists and clinicians move faster, with confidence. From codifying guardrails to enabling self-service deployments and AI integrations, your work will turn complex ideas into reliable, compliant, and cost-efficient solutions. Do you thrive in a high-ownership, fast-paced environment where your code unlocks impact at enterprise scale? Accountabilities Cloud Platform Automation: Build, automate, and maintain AWS infrastructure using Terraform, CloudFormation, or similar declarative configuration tools to deliver repeatable, secure environments. CI/CD Enablement: Design and maintain pipelines in Jenkins, GitHub Actions, and GitLab CI to ship changes safely and frequently, embedding quality gates and controls. Secure-by-Design Engineering: Implement IAM the least privilege, secrets management, network segmentation, and governance guardrails that meet compliance without slowing delivery. Containerized Workloads: Package and run services with Docker on ECS/EKS and orchestrate event-driven compute with Lambda for scalable, resilient apps. Observability and Reliability: Instrument logging, metrics, and tracing using CloudWatch, Datadog, and Grafana; automate alerting and remediation; drive performance, reliability, and cost efficiency. Reusable Platform Capabilities: Develop IaC patterns, blueprints, and self-service offerings that unblock engineering and data teams and set the standard on consistency. Data and Integration Enablement: Provide patterns for data processing and transformation; integrate with enterprise APIs, databases, and cloud services; leverage messaging with SQS/SNS/Event Bridge. GenAI on AWS: Develop Python integrations with AWS Bedrock, foundation models, LLM APIs, and embedding services; deploy production-ready AI-enabled applications with monitoring and guardrails. RAG Solutions: Implement document ingestion, chunking, vectorization, retrieval, and grounded response generation to deliver reliable Retrieval-Augmented Generation. AI Operations and Evaluation: Monitor AI applications, analyze performance, optimize inference costs, and assist with timely engineering, model selection, evaluation, and troubleshooting. Ways of Working: Collaborate closely with cloud, DevOps, data, and machine learning groups; integrate AI workloads into CI/CD and MLOps processes; chip in to engineering standards and mentor peers as capabilities scale. Essential Skills/Experience 4–6 years of experience in Amazon Web Services, continuous integration and delivery, Cloud Engineering, or a similar software engineering role. Strong hands-on experience using AWS services such as EC2, ECS, S3, Lambda, IAM, CloudWatch, VPC, Load Balancers, SQS, and SNS. Experience with automated build and deployment pipelines and automation tools, including Jenkins, GitHub Actions, GitLab CI, and/or Ansible. Practical experience working with Infrastructure as Code, preferably Terraform and/or CloudFormation. Good understanding of AWS networking, security, IAM, and least privilege principles. Strong proficiency with Git/GitHub/Bitbucket and source-code management practices. Good knowledge of Linux and Windows infrastructure, including Linux administration, Bash, and shell scripting, strong understanding of Docker and containerization. Experience with observability and monitoring tools such as AWS CloudWatch, Datadog, and Grafana. Strong proficiency in Python 3.x for automation, scripting, API development, and cloud-native applications. Good understanding of software engineering practices including unit testing, code quality, packaging, logging, exception handling, and version control. Experience developing or consuming REST APIs and integrating cloud and enterprise services. Working knowledge of Generative AI, LLMs, prompt engineering, embeddings, and RAG concepts. Hands-on/project experience working on AWS Bedrock or an alternative managed GenAI platform. Experience using Python to integrate with LLM APIs, AI services, or foundation models. Basic understanding of RAG architecture and vector search. Understanding of deploying AI applications using Docker and AWS ECS/EKS/Lambda. Foundational knowledge of MLOps/LLMOps, including deployment automation, versioning, monitoring, evaluation, and observability. Strong problem-solving perspective with a Lean and efficiency-focused approach. Proactive, diligent, and dedicated to achieving goals. Collaboration and communication skills with proficiency in engaging effectively across technical teams. Capacity to work independently while contributing effectively within a team environment. Familiarity with DevOps and Agile principles. Understanding of Scrum and Kanban methodologies. Strong interest in emerging AI/Generative AI technologies and continuous learning. Bachelor’s or master’s degree or equivalent experience in Computer Science, Information Technology, a technical field, or a related subject area, and demonstrated experience across AWS Cloud, DevOps, Python automation, CI/CD, and exposure to AI/Generative AI technologies. Desirable Skills/Experience Exposure to scalable and High Availability (HA) AWS architectures. Exposure to Kubernetes/EKS administration, Helm, and GitOps/Argo CD. Experience with AWS EFS, RDS, DynamoDB, Secrets Manager, Event Bridge, Service Catalog, and Cost Management. Experience implementing automated monitoring, alerting, remediation, and self-healing. Exposure to event-driven architectures using EventBridge, SQS, SNS, or Kafka. Exposure to AWS SageMaker, Databricks, Snowflake, MLflow, or Hugging Face. Exposure to Strands Agents, LangChain, LlamaIndex, or MCP (Model Context Protocol). Experience building basic AI agents or tool-enabled LLM applications. Experience with AI/LLM evaluation, model performance monitoring, and LLM observability. Understanding of prompt/model caching and GenAI inference-cost optimization. Familiarity with AI security concepts such as PII protection, prompt injection, access control, and data governance. Exposure to Airflow, dbt, Kafka, or other data orchestration technologies. AWS certifications such as Cloud Practitioner, Solutions Architect – Associate, Developer – Associate, Data Engineer – Associate, or AI/ML certifications are a plus. Why AstraZeneca Here, your engineering craft directly empowers science to move faster for patients. You will work in a high-energy environment that brings unexpected combinations of talent into the same room to spark bold thinking, where cloud engineers, data specialists, and AI practitioners co-create solutions end to end. We blend ambition with patience, pairing modern technology with a collaborative spirit so you can take smart risks, learn at speed, and see your ideas land in production. Your contribution will shape platform capabilities used across the enterprise, giving you the scope to build an exceptional reputation while making a tangible difference to patient outcomes. Call to Action If you are ready to build secure, automated, AI-enabled platforms that accelerate substantial impact, share your profile and show us how you will lead the next wave of engineering at AstraZeneca! We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Date Posted 07-Sept-2026 Closing Date 20-Sept-2026 AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.