AWS DevOps & AI Engineer

AstraZeneca; India; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

AstraZeneca is seeking a Cloud Platform Engineer to scale secure AWS infrastructure, enable CI/CD, and operationalize GenAI workloads. You will join a cross-functional platform team delivering reusable cloud capabilities, guardrails, and self-service deployments that empower data, engineering, and AI/ML groups.

Role focuses on secure-by-design engineering, IaC (Terraform/CloudFormation), Docker/Kubernetes, and Python automation, with emphasis on scalable, compliant, cost-efficient solutions

Qualifications

• 4–6 years of experience in AWS, CI/CD, or cloud engineering.
• Hands-on with EC2, ECS, S3, Lambda, IAM, CloudWatch, VPC, SQS, SNS.
• Experience building automated pipelines and IaC (Terraform/CloudFormation).
• Strong Linux skills, Bash scripting, and containerization with Docker.
• Familiarity with observability tools like CloudWatch, Datadog, Grafana.
• Proficient in Python for automation and cloud-native apps.
• Understanding of REST APIs and enterprise integrations.
• Knowledge of GenAI, LLMs, embeddings, RAG, and MLOps concepts.
• Ability to work across cloud, data, DevOps, and AI teams.

Responsibilities

• Cloud Platform Automation: build, automate, and maintain AWS infra with IaC.
• CI/CD Enablement: design pipelines in Jenkins, GitHub Actions, GitLab CI with quality gates.
• Security by design: implement least privilege, secrets management, and governance guardrails.
• Containerized workloads: run services with Docker on ECS/EKS, use Lambda for event-driven apps.
• Observability: instrument logging, metrics, tracing; automate alerting and remediation.
• Reusable platform capabilities: create IaC patterns and self-service blueprints.

Skills

AWS

CI/CD

Python

Git & SCM

Linux

Problem solving

Collaboration

MLOps

LLM APIs

Tools

Terraform

CloudFormation

Jenkins

GitHub Actions

GitLab CI

Ansible

Docker

Kubernetes

AWS Bedrock

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! 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.
• Familiarit