Job
Description
We are looking for an experienced AWS-focused DevOps/Cloud Engineer with 8+ years of experience in cloud infrastructure, automation, and CI/CD pipelines. The ideal candidate should have at least 1 year of experience supporting AI/ML workloads , including vector databases, AI-driven video processing, and text-to-video applications . Key Responsibilities: Design, implement, and manage scalable, secure, and highly available AWS-based infrastructure. Support AI/ML initiatives , including deploying vector databases (FAISS, Pinecone, Weaviate) and managing AI-powered video processing workflows. Develop and maintain CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI/CD, or AWS CodePipeline. Automate infrastructure provisioning and configuration using Terraform, Ansible, or AWS CloudFormation . Optimize cloud resources for AI/ML workloads, including GPU instances (AWS EC2 with NVIDIA GPUs, AWS Inferentia, AWS Trainium). Implement monitoring, logging, and alerting using AWS CloudWatch, AWS X-Ray, and OpenTelemetry . Ensure security and compliance using AWS IAM, AWS Organizations, AWS Security Hub, and AWS KMS . Troubleshoot and resolve infrastructure, networking, and performance issues in AWS environments. Required Skills & Experience: 8+ years of experience in DevOps, Cloud Engineering, or Infrastructure Automation . Strong expertise in AWS cloud services (EC2, S3, RDS, VPC, Lambda, SageMaker, etc.). Hands-on experience with Terraform, Ansible, or AWS CloudFormation . Experience with AI/ML model deployment using SageMaker, TensorFlow Serving, MLflow, or AWS Batch . Experience with vector databases (FAISS, Pinecone, Weaviate) and AI-powered search. Understanding of AI-based video processing (AWS MediaConvert, AWS Rekognition, or custom AI pipelines for text-to-video conversion). Proficiency in scripting (Bash, Python, Go, or PowerShell) for automation . Strong knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, AWS CodePipeline). Experience with GPU workloads and AWS AI/ML services (AWS Inferentia, AWS Neuron SDK, SageMaker, AWS Bedrock). Strong understanding of networking, security best practices, and IAM policies . Experience with monitoring/logging tools (AWS CloudWatch, AWS X-Ray, Prometheus, Grafana). Nice-to-Have Skills: Knowledge of serverless computing (AWS Lambda, Step Functions, EventBridge) . Experience with Kafka, RabbitMQ, or AWS EventBridge/SNS/SQS . Experience working in a UK time zone or distributed team setup. Exposure to AWS MLOps tools like SageMaker Pipelines, AWS Step Functions, or Kubeflow on AWS .
