AWS DevOps & AI Engineer

AstraZeneca; Chennai District; On-site; INR 2,200,000 - 3,500,000; Full time

Job summary

AstraZeneca is seeking an experienced Cloud Platform Engineer to scale secure, automated AWS platforms and operationalize GenAI in production. You will work with platform, data, and AI/ML teams to deliver reusable cloud capabilities and guardrails that accelerate life sciences initiatives.

Responsibilities include building IaC, enabling CI/CD, deploying containerized workloads, and ensuring observability and cost efficiency.

Qualifications

• 4–6 years in AWS, CI/CD, Cloud Engineering, or similar software engineering roles.
• Hands-on with AWS services (EC2, ECS, S3, Lambda, IAM, CloudWatch, VPC, Load Balancers, SQS, SNS).
• Experience with automated build and deployment pipelines (Jenkins, GitHub Actions, GitLab CI) and IaC (Terraform/CloudFormation).
• Strong knowledge of Linux, Bash, Docker, and containerization.
• Proficient in Python 3.x for automation and API development.
• Familiar with REST APIs and DevOps/Agile practices; knowledge of GenAI concepts welcome.

Responsibilities

• Cloud Platform Automation: Build, automate, and maintain AWS infrastructure to deliver repeatable, secure environments.
• CI/CD Enablement: Design and maintain pipelines to ship changes safely with quality gates.
• Secure-by-Design Engineering: Implement least privilege IAM, secrets management, and governance guardrails.
• Containerized Workloads: Package and run services with Docker on ECS/EKS, orchestrate with Lambda.
• Observability and Reliability: Instrument logging and metrics; automate alerting and remediation.
• Reusable Platform Capabilities: Develop IaC patterns and self-service offerings for teams.
• Data and Integration Enablement: Patterns for data processing; integrate with APIs and cloud services.
• GenAI on AWS: Develop Python integrations with Bedrock/LLMs; deploy AI-enabled apps with monitoring.
• RAG Solutions: Implement document ingestion, vectorization, retrieval, and grounded response generation.
• AI Operations and Evaluation: Monitor AI apps, optimize costs, assist with model selection and troubleshooting.
• Ways of Working: Collaborate across cloud, data, and ML teams; contribute to standards and mentoring.

Skills

Python automation

CI/CD pipelines

Infrastructure as Code

Docker and containers

Observability

GenAI / LLMs

Team collaboration

Education

Bachelor's or Master's degree in Computer Science / IT

Tools

Terraform

CloudFormation

Jenkins

GitHub Actions

GitLab CI

Ansible

Docker

ECS/EKS

Lambda

CloudWatch

Datadog

Grafana

Bedrock

LangChain

LlamaIndex

Kubernetes

Argo CD

SageMaker

Job description

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!

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!

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.

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.