• Operate and evolve the hybrid environment (bridging AWS and On-prem resources) • Maintain and scale PostgreSQL databases, Kafka clusters, and data pipelines ("data grabbers") • Support High-Performance Computing (HPC) environments for ML modeling • Advance Infrastructure as Code (IaC) using AWS CDK (TypeScript) and automation via Ansible/Bash • Manage access control (IAM, Cloud & On-prem users) and implement security best practices across the hybrid environment • Develop and promote a self-service DevOps model for the development teams

• Work with and debug our current stack: GitHub, Jenkins, GitOps, k8s, Flux CD, Databricks and help design our future stack (i.e. Argo CD) with self-service • Cut deep into systems in order to drive directly to a resolution and develop the source code in order to make it possible • Face a variety of challenges that will allow you to constantly expand your repertoire by always learning new complex systems and skills • Use your expert knowledge of SDLC, integrating systems through code in order to improve tooling and processes

• Evaluate AI outputs related to software engineering, DevOps, and infrastructure topics • Perform structured scoring, comparison, classification, and judgment tasks • Assess technical correctness, completeness, security implications, and best-practice alignment • Identify hallucinations, incorrect code, unsafe recommendations, or misleading system guidance • Apply domain-specific engineering and DevOps guidelines consistently across tasks

• Evaluate AI outputs related to software engineering, DevOps, and infrastructure topics • Perform structured scoring, comparison, classification, and judgment tasks • Assess technical correctness, completeness, security implications, and best-practice alignment • Identify hallucinations, incorrect code, unsafe recommendations, or misleading system guidance • Apply domain-specific engineering and DevOps guidelines consistently across tasks • Validate and refine evaluation rubrics and edge-case handling • Perform adjudication where raters disagree • Conduct error analysis and qualitative reviews of model behavior • Partner with LILT research, product, and customer teams on evaluation design • Support red-teaming, security review, and model readiness assessments