DevOps Engineer

We’re seeking an experienced Senior DevOps Engineer to join our engineering team. In this role, you’ll collaborate with software developers, ML Engineers, QA engineers, and operations to design, build, and maintain scalable, secure, and highly available infrastructure and deployment pipelines in both AWS and Azure. You’ll drive the adoption of Infrastructure as Code, oversee our Bitbucket-based source workflows, champion best practices in automation and observability, and help bring our AI-integrated software solutions into production. Required Qualifications

3+ years in DevOps, Site Reliability, or Cloud Engineering roles, with demonstrable ownership of production services.

Proven track record working in AWS and Azure environments.

Advanced proficiency CloudFormation, or Azure Resource Manager (ARM/Bicep).Strong scripting skills (Python, Bash, PowerShell).

Hands-on experience with Bitbucket, Bitbucket Pipelines, or other Git-based workflows.

Container and orchestration expertise (Docker, Kubernetes/EKS/AKS).Familiarity with AI/ML deployment tools (MLflow, Kubeflow, SageMaker, Azure ML).

Solid experience in monitoring, logging, and alerting frameworks across multi-cloud.

Deep understanding of cloud security, IAM, and secrets management.

Soft Skills

Excellent problem-solving aptitude and strong communication skills.

Ability to work cross-functionally in agile teams and mentor peers.

Preferred Qualifications

Certifications such as AWS Certified DevOps Engineer, Microsoft Certified: Azure DevOps Engineer, or Certified Kubernetes Administrator (CKA).

Background deploying real-time AI/ML inference services at scale.

Knowledge of service meshes (Istio, Linkerd).

Responsibilities

Define standards for networking, storage, compute, and identity across clouds; optimize cost and performance.

Build and maintain robust CI/CD pipelines in Bitbucket Pipelines (or integrating with Jenkins/GitHub Actions as needed) for microservices and AI model deployments.

Automate entire lifecycle—from code commit through container build, model packaging, testing, and rollout.

Integrate machine learning model training and inference into infrastructure pipelines, ensuring reproducibility and version control for data, code, and models.

Securely manage credentials and secrets with AWS Secrets Manager, Azure Key Vault, or Vault.

Implement comprehensive logging and monitoring (Prometheus, Grafana, ELK/EFK, Azure Monitor, CloudWatch).

Enforce security best practices across pipelines and infrastructure: IAM policies, vulnerability scanning, container security, network segmentation.

Partner closely with AI/ML engineers, software developers, and product owners to align infrastructure with business goals.