Cloud engineer

At go ML, we design and build cutting-edge Generative AI, AI / ML, and Data Engineering solutions that help businesses unlock the full potential of their data, drive intelligent automation, and create transformative AI-powered experiences. Our mission is to bridge the gap between state-of-the-art AI research and real-world enterprise applications – helping organizations innovate faster, make smarter decisions, and scale AI solutions seamlessly.

We’re looking for a Cloud Engineer with strong AWS expertise and hands-on experience in designing, deploying, and optimizing scalable cloud environments. In this role, you’ll architect and manage secure, cost-efficient, and high-performance cloud infrastructure that powers AI / ML and Gen AI solutions at enterprise scale. If you thrive in solving complex cloud challenges and enabling teams with reliable, cloud-native systems, we’d love to hear from you!

Why You? Why Now?

As AI adoption accelerates, cloud infrastructure becomes the foundation that enables enterprises to scale their AI / ML workloads. This role is ideal for someone who loves building cloud-native architectures, optimizing infrastructure, and ensuring cloud environments are production-ready for AI.

What You’ll Do (Key Responsibilities)

First 30 Days : Foundation & Orientation

Deep dive into go ML’s AI / ML & Gen AI workloads and cloud architecture patterns

Familiarize yourself with AWS environments, networking, and monitoring frameworks at go ML

Review existing infrastructure and identify optimization opportunities

Shadow AI / ML teams to understand cloud requirements for model training and inference First 60 Days : Execution & Impact

Design, deploy, and manage AWS infrastructure with services like EC2, ECS, EKS, Lambda, VPC, and RDS

Implement cloud networking and security best practices (VPCs, IAM, API Gateway, Load Balancers)

Automate infrastructure provisioning using Terraform, AWS CDK, or Cloud Formation

Set up observability and monitoring dashboards with Cloud Watch and third-party tools

Collaborate with Dev Ops and AI / ML engineers to ensure seamless cloud integration First 180 Days : Ownership & Transformation

Own cloud architecture for AI / ML and Gen AI enterprise deployments

Optimize infrastructure for performance, scalability, and cost-efficiency

Build disaster recovery, backup, and high-availability strategies

Establish best practices for cloud security, governance, and compliance

Mentor junior engineers and influence long-term multi-cloud strategies (AWS, Azure, GCP)

What You Bring (Qualifications & Skills)

Must-Have :

2-4 years of experience in cloud engineering with strong AWS expertise

Hands-on experience with core AWS services (EC2, VPC, S3, RDS, Lambda, ECS, EKS, API Gateway, Load Balancers)

Proficiency with Ia C tools like Terraform, AWS CDK, or Cloud Formation

Strong understanding of cloud networking, IAM, and security best practices

Experience with monitoring, logging, and observability (Cloud Watch, ELK, Grafana, etc.)

Scripting experience (Python, Bash, or Shell) for automation

Excellent troubleshooting and communication skills Nice-to-Have :

AWS Certified Solutions Architect or AWS Certified Sys Ops Administrator

Exposure to AI / ML infrastructure (Sage Maker, Bedrock)

Familiarity with Azure and GCP cloud environments

Why Work With Us?

Remote-first, with offices in Coimbatore for in-person collaboration

Work on cutting-edge AI / ML & Gen AI cloud challenges at scale

Direct impact on enterprise cloud architecture and AI deployments

Competitive salary, leadership growth opportunities, and ESOPs down the line