Capstoneintegratedsolutions

Overview

Capnexus is looking for a highly motivated and experienced AWS DevOps Engineer / MLOps to join our growing team, focusing on cloud, automation, and AI-enabled infrastructure solutions in enterprise AWS environments.

About Capstoneintegratedsolutions

Capnexus is a comprehensive services provider. Our team consists of outstanding professionals, highly experienced in designing, building, and supporting retail software. We see ourselves as a build-as-a-service provider who follows a repeatable business pattern that can be applied to a variety of platforms and verticals.

Requirements & Eligibility

• 4+ years of DevOps or cloud infrastructure experience, with at least 2+ years on AWS.
• Hands-on experience with CI/CD tooling (GitHub Actions, AWS CodePipeline, Jenkins, or equivalent) in a multi-environment AWS setup.
• Proficiency with infrastructure as code — AWS CDK, CloudFormation, or Terraform.
• Working knowledge of MLOps practices on Amazon SageMaker — model deployment, endpoint management, monitoring, and pipeline automation.
• Experience with AWS IAM, Lake Formation, VPC, and security best practices for data environments.
• Familiarity with AWS monitoring and observability tools — CloudWatch, CloudTrail, AWS Config.
• Experience supporting data pipeline infrastructure — AWS Glue, Step Functions, Lambda, Kinesis, and S3.
• Strong understanding of environment management, change control processes, and rollback procedures in enterprise delivery contexts.

Key Responsibilities

• Design and maintain CI/CD pipelines to support automated testing, deployment, and change management across CDP, data lake, marketing cloud, and migration.
• Provision and manage AWS environments (S3 buckets, Glue jobs, networking, IAM, VPCs) for non-production development and staging, ensuring environments are logically segregated with no public internet access per SOW requirements.
• Implement and enforce infrastructure as code (IaC) using AWS CDK or Terraform to ensure reproducible, auditable environment configurations across all workstreams.
• Manage MLOps lifecycle on Amazon SageMaker — including model versioning, deployment automation, endpoint monitoring, and automated retraining triggers for lead scoring, predictive maintenance, underwriting, and audience segmentation models.
• Support Amazon Bedrock integration deployments — managing prompt versioning, model configuration, and API endpoint reliability.
• Implement and maintain data lake security infrastructure using AWS Lake Formation, including row-level security, column-level encryption, and IAM permission boundaries.
• Support Azure to AWS migration execution — provisioning target AWS environments, coordinating AWS DataSync jobs, managing cutover procedures, freeze periods, and rollback plans.
• Monitor pipeline health, data job execution, and ML endpoint performance across AWS Glue, Step Functions, Lambda, Kinesis, and SageMaker using CloudWatch and AWS-native observability tools.