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Senior DevOps Engineer, AWS, AI Infrastructure
Software MindSenior DevOps Engineer at Software Mind managing AWS infrastructure for AI assistants. Collaborating with a cross-functional LATAM team to deliver tenant-isolated cloud solutions.
Tech Stack
Tools & technologiesAWSCloudDynamoDBRayTerraform
About the role
Key responsibilities & impact- Provision and configure a dedicated VPC and segmented cloud environment on AWS
- Build the baseline CI/CD pipeline and maintain and evolve it across all delivery phases
- Configure and manage the vector store infrastructure (OpenSearch/Pinecone on AWS)
- Set up and manage the observability stack: CloudWatch, X-Ray, alerting thresholds, and LLM-specific monitoring
- Implement infrastructure-as-code for all environments (dev, staging, production) using Terraform or CDK
- Manage secrets, KMS encryption key configuration, and tenant-scoped access controls
- Configure LLM provider connectivity (OpenAI / Anthropic / Amazon Bedrock enterprise tier, zero-data-retention)
- Define and implement environment promotion strategy aligned with the 2-week sprint cadence
- Support incremental ingestion pipeline infrastructure requirements and nightly scheduling
Requirements
What you’ll need- 6+ years in DevOps or cloud infrastructure engineering; strong AWS specialisation required
- Infrastructure-as-code: Terraform, CloudFormation, or AWS CDK
- CI/CD tooling: GitHub Actions, AWS CodePipeline, or equivalent
- Core AWS services: VPC, ECS, Lambda, S3, DynamoDB, API Gateway, Cognito, CloudWatch, X-Ray
- Experience designing and operating multi-tenant cloud environments with tenant-level data isolation
- AI Experience (Required Not Optional)
- At least one project operating infrastructure for a production AI/ML or LLM-integrated system not just general cloud workloads
- Experience configuring and managing vector store infrastructure (OpenSearch, Pinecone, Weaviate, or equivalent) in a production environment
- Familiarity with LLM provider APIs (OpenAI, Anthropic, or Amazon Bedrock) in a production/enterprise configuration, including zero-data-retention tier setup
- Understanding of AI-specific observability concerns: token usage monitoring, latency profiling for LLM calls, and model response logging
Benefits
Comp & perks- Flexible work arrangements
- Professional development opportunities
ATS Keywords
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Hard Skills & Tools
AWSTerraformCloudFormationAWS CDKCI/CDGitHub ActionsAWS CodePipelineOpenSearchPineconeAI/ML