Overview::

We are seeking a hands-on Senior Cloud DataOps & MLOps Engineer to help build and operate a modern AWS-based monitoring and machine learning platform environment. In this role, you will bridge the gap between data operations, machine learning, and cloud infrastructure to enable scalable, observable, and reliable systems.

Key focus areas include::

Setting up and managing Amazon SageMaker environments
Implementing centralized monitoring and observability using Prometheus and Grafana
Building logging and metrics pipelines across AWS applications and services
Supporting cloud platform automation, operational monitoring, and MLOps enablement

The ideal candidate will have a strong cloud engineering foundation and practical experience operating enterprise-scale AWS environments.

Key Responsibilities::

Design and implement AWS cloud infrastructure and operational platforms
Configure and manage Amazon SageMaker environments to support ML workflows
Set up centralized monitoring, metrics collection, and alerting using Prometheus and Grafana
Build logging and observability pipelines across applications and AWS services
Develop dashboards, alerts, and operational monitoring standards
Support platform automation using Infrastructure-as-Code (IaC) and CI/CD pipelines
Work closely with application, data, and platform teams to improve reliability and operational visibility
Troubleshoot production issues and optimize cloud platform performance

Required Skills & Experience::

Mandatory

Strong hands-on experience with AWS cloud services including:

1.IAM, VPC, EC2, S3, CloudWatch

2.ECS or EKS

3.Security and networking fundamentals

Proven experience with:

1.Prometheus and Grafana

Monitoring and observability platforms
Centralized logging architectures
Experience with Amazon SageMaker or other MLOps platforms
Proficiency with Terraform, CloudFormation, or similar IaC tools
Scripting experience in Python or Bash
Experience supporting production cloud environments

Preferred::

Kubernetes / EKS experience
OpenTelemetry experience
CI/CD pipeline implementation
DataOps or platform engineering background
AWS certifications (e.g., Solutions Architect, DevOps Engineer, or Machine Learning Specialty)

Ideal Candidate Profile::

We are looking for someone who is:

Hands-on and technically strong – comfortable diving into infrastructure and code
Enterprise-ready – experienced in navigating complex cloud environments
A strong troubleshooter – able to diagnose and resolve production issues efficiently
Proactive and independent – takes ownership and drives tasks to completion
A clear communicator – able to collaborate effectively with technical teams and business stakeholders

Technology Environment::

Nice-to-Have::

Experience supporting AI/ML platforms in production
Experience with enterprise observability frameworks (e.g., OpenTelemetry, Jaeger)
Multi-account AWS environment experience (e.g., AWS Organizations, Control Tower)
FinOps or cloud governance exposure (e.g., AWS Cost Explorer, Budgets, tagging strategies)

Cloud Data Platform Engineer