Trustana

LLM Engineer

• Research, prototype, and productionize LLM solutions for tasks such as product classification, enrichment, and workflow automation.
• Fine-tune and optimize foundation models for domain-specific performance.
• Build scalable inference pipelines and APIs to deliver AI features in production.
• Partner with backend, data, and product teams to define requirements and deliver impactful AI solutions.
• Benchmark model performance and optimize across accuracy, latency, and cost.
• Stay current on advances in LLMs, multimodal models, and tooling, bringing relevant improvements into production.
• (L4) Take technical ownership of major AI features, mentor junior engineers, and influence architecture and design decisions.

QA Engineer

• Design, build, and maintain automated test frameworks (API, integration, and UI where it makes sense)
• Own automated test execution in CI/CD pipelines
• Reduce flaky tests and improve test reliability and runtime
• Work with QA peers who define test cases and scenarios, translating them into automation
• Partner with engineers to improve testability of the product
• Help define what should be automated vs what should remain manual
• Track automation coverage and gaps over time
• Raise quality issues early, with clear evidence

Senior QA Engineer – Automation

Platform Engineer

• Design, build, and maintain internal developer platforms and infrastructure that support high-scale microservices and serverless systems (AWS ECS, Lambda, Terraform, etc.)
• Partner with engineering teams to improve deployment pipelines (GitLab CI/CD, Docker, Kubernetes, Terraform)
• Optimize observability: set up and improve monitoring, alerting, and logging (Datadog, Prometheus, Grafana, etc.)
• Improve systems reliability and incident response (SLOs, SLIs, on-call processes, chaos testing)
• Manage infrastructure as code and reduce manual operations across environments
• Contribute to platform security with IAM best practices, secret management, and supply chain protections
• Support data services scaling (Postgres, Redis, S3), and help productionize AI/ML models through efficient deployment workflows