DevOps MLOps Engineer

What you'll work on

DevOps

• CI/CD pipelines – Automated build, test, and deploy on every push
• Infrastructure as code – Terraform/Pulumi for reproducible environments
• Monitoring & alerting – Know when things break before customers do
• Incident response – Own uptime and reliability
• Daily deploys – Enable the team to ship to production every day safely

MLOps

• Model serving infrastructure – Deploy and serve LLMs (local and API-based)
• Model router – Build the abstraction layer that routes requests to appropriate models
• GPU infrastructure – Manage inference servers for local models (Llama, Mistral)
• Cost optimization – Track and optimize model usage costs
• Model versioning – Safe rollouts and rollbacks for prompt/model changes

Platform

• Developer experience – Make the team faster through better tooling
• Scaling – Prepare infrastructure for growth

Security (Critical)

• Infrastructure security – Server hardening, network security, firewall configuration, VPC design
• Secrets management – Vault, AWS Secrets Manager, or similar; no secrets in code
• Access control – IAM policies, least-privilege principles, SSO integration
• Vulnerability scanning – Automated scanning in CI/CD, dependency audits, container scanning
• Intrusion detection – CloudTrail, GuardDuty, or similar; alert on suspicious activity
• Encryption – Data at rest and in transit; key management
• Incident response – Work with fractional CISO to implement detection, containment, and recovery procedures
• Compliance – Support audits and maintain security documentation

Quality & Testing Infrastructure

• CI/CD quality gates – Automated tests run on every push; bad code doesn't deploy
• Test environment management – Staging environments that mirror production
• LLM output monitoring – Track hallucinations, wrong tool calls, response quality in production
• Security scanning – Automated vulnerability scanning in CI pipeline
• Alerting & anomaly detection – Know when something breaks before customers do

Tech StackCurrent

• Cloud: AWS (EC2, RDS, S3, Lambda)
• Containers: Docker
• CI/CD: GitHub Actions
• Database: PostgreSQL (RDS)
• Caching: Redis

You'll Build

• Model serving: vLLM, Ollama, or similar for local inference
• GPU compute: AWS/GCP GPU instances or dedicated inference providers
• Model routing: Custom abstraction layer for model selection
• Observability: Datadog, Grafana, or similar for unified monitoring

What we're looking for

• MLOps experience – Model deployment, serving, monitoring
• GPU infrastructure – Managing inference workloads
• Experience with LLM serving (vLLM, TGI, Ollama)
• Kubernetes experience
• Cost optimization mindset

• Experience serving both frontier APIs and local models
• LangChain/LangSmith or similar LLM observability
• Startup experience – comfort with ambiguity and speed
• Texas location