Responsibilities
- Design and maintain CI/CD pipelines to support automated testing, deployment, and change management across CDP, data lake, marketing cloud, and Azure 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.
- Implement cost optimization practices across all AWS services; provide guidance on Azure resource decommissioning post-migration.
- Coordinate change management processes per SOW requirements — documenting changes, obtaining approvals, executing peer-reviewed deployments, and maintaining rollback procedures.
- Collaborate with Data/ML and Full Stack engineers to ensure smooth integration across data pipelines, ML inference, and application layers.
- Contribute to operations runbooks, deployment guides, and infrastructure documentation.
Requirements
- 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.
- Ability to work in Agile/Scrum teams alongside AWS Professional Services with structured change approval workflows.
Nice to Have
- Experience with Azure infrastructure and Azure-to-AWS migration (DataSync, networking, decommissioning).
- Familiarity with Amazon Bedrock deployment and API management.
- Experience with Amazon Connect or omnichannel platform infrastructure.
- Knowledge of data compliance and privacy controls (encryption at rest/in transit, access logging, audit trails).
- AWS Certification (DevOps Engineer Professional, Solutions Architect, or Machine Learning Specialty).
- Exposure to Kiro CLI or AI-assisted infrastructure tooling.
- Background in real estate, property management, or enterprise SaaS environments.
Benefits
- Remote work
Additional Information
- Capnexus is an equal opportunity employer.
- We embrace and celebrate diversity and are committed to creating an inclusive and safe environment for all employees.
- We encourage you to apply even if your experience doesn't perfectly align with what we have listed.
