Senior AI Engineer

We’ve come a long way since we first opened our doors, but our mission has always stayed the same: to provide world class solutions for the travel industry. Travel Booster is a constantly improving ERP solution that frees users from the complexities of creation so they can do more, faster. We are looking for a hands‑on engineer to own our cloud infrastructure and CI/CD pipelines on AWS with Azure DevOps (Pipelines & Releases). You will take over existing infrastructure and pipelines written in PowerShell and Python, stabilize & improve them, and lead AI/LLM enablement across Dev, QA, and Support—including building internal agents, integrating with LLMs (via MCP and other frameworks), and driving AI adoption in day‑to‑day workflows. You’ll partner closely with R&D to streamline builds, deployments, environment management, and observability, while also pushing upgrades and optimizations in AWS services.What will your job look like?Key ResponsibilitiesCI/CD & Cloud InfrastructureOwn, maintain, and enhance Azure DevOps Pipelines & Releases (multi‑stage YAML/classic) for builds, deployments, and scheduled tasks.Refactor existing PowerShell, javascript and Python pipeline scripts for reliability, security, and performance.Implement pipeline templates, gates, approvals, artifact versioning, and automated rollbacks.Introduce pipeline observability (dashboards, alerts, metrics) and enforce branching, tagging, and release standards.Operate and improve AWS environments (Prod/Stage/Dev/QA), including networking, compute, storage, databases, security, and cost management.Implement Infrastructure as Code (IaC) (Terraform or AWS CloudFormation); manage parameter stores/secrets.Automate environment lifecycle: provisioning, configuration, blue/green & canary releases, and disaster recovery.Drive updates and improvements across services (e.g., EKS/EC2/Lambda, RDS/DynamoDB, ElastiCache, S3, CloudFront/ALB/ELB, Route 53, CloudWatch, Systems Manager, IAM, KMS).Standardize and automate Dev/QA/SIT/UAT environments; ensure parity with Production where relevant.Build ephemeral preview environments per PR; manage test data seeding/anonymization.Collaborate with QA to integrate test orchestration (smoke/regression) into pipelines with gates and quality metrics.Implement monitoring/alerting (CloudWatch, Prometheus/Grafana), logging (CloudWatch Logs/OpenSearch), and distributed tracing (X-Ray/OpenTelemetry).Harden IAM (least privilege, scoped roles), secrets management, key rotation, and compliance checks.Track and optimize AWS spend (rightsizing, autoscaling, lifecycle policies).AI/LLM EnablementBuild and deploy AI-powered features using LLMs (OpenAI, Azure OpenAI, Hugging Face).Implement MCP-based integrations for context-aware workflows.Design and build internal AI agents (for Dev/QA/Support) to assist with triage, runbooks, test data generation, log analysis, and developer productivity.Integrate LLMs via secure patterns (e.g., Model Context Protocol (MCP), retrieval‑augmented generation, prompt engineering, guardrails, and observability).Create tooling that plugs AI into pipelines/workflows (PR summaries, release notes, changelog generation, incident post‑mortems).Establish data governance for AI: access control, PII handling, prompt/data sanitization, and auditability.Write production-grade code for AI workflows, including prompt engineering, function calling, and agent orchestration.Integrate AI services into ERP, web apps, and backend systems.