AWS DevOps Engineer

About Us

Testing Mavens is a next-generation QA & Software Testing Services provider headquartered in New Jersey, USA, with offshore facilities in India. We have deep expertise in various spheres like Finance and Advisory, EdTech, Fashion and Pharmacy, etc. We partner with massive global brands to deliver excellent testing experience.

Test Engineer

Kochi, Kerala or Permanent Remote (Anywhere in India)

Full time

Job Overview

We are seeking an AWS DevOps Engineer to design, implementation, and maintenance of scalable, secure, and production-grade cloud infrastructure. This role is ideal for someone with deep DevOps expertise and a strong interest or background in deploying LLM-based and agentic AI systems at scale.

Key Responsibilities

• Architect and implement robust AWS infrastructure using Infrastructure as Code (Terraform, CloudFormation, or CDK)

• Lead development of CI/CD pipelines using AWS CodePipeline, CodeBuild, GitLab CI, or Jenkins

• Manage and scale containerized applications using Docker, ECS, EKS, and Kubernetes

• Define and maintain monitoring, logging, and alerting using CloudWatch, ELK, Prometheus, or Grafana

• Enforce security best practices, cost optimization, and ensure high availability of infrastructure

• Collaborate with ML engineers to productionize ML/LLM pipelines, including agentic and retrieval-augmented generation (RAG) systems

   

Required Skills

• 5+ years of experience in AWS cloud infrastructure (EC2, S3, VPC, Lambda, RDS, ECS/EKS)

• Proven track record in DevOps practices and building complex CI/CD pipelines

• Expertise in Infrastructure as Code (Terraform, CloudFormation, or AWS CDK)

• Strong proficiency in container orchestration (Kubernetes, Docker)

• Scripting skills (Python, Bash) and solid Linux administration experience

• AWS certifications (Solutions Architect, DevOps Engineer, or equivalent) preferred

   

Preferred Skills

• Experience deploying LLMs and agentic AI systems in production (e.g., autonomous agents, RAG pipelines, tool-using LLMs)

• Familiarity with AWS AI/ML services (SageMaker, Bedrock, Lambda for inference, etc.)

• MLOps experience with tools like MLflow, Kubeflow, SageMaker Pipelines

• Experience with event-driven or streaming architectures (Airflow, AWS Glue, Kafka)

• Advanced observability for AI/ML systems (model drift, custom monitoring, Grafana dashboards)

• Familiarity with securing and scaling AI inference endpoints in enterprise environments