Ana G.

Senior .NET Engineer | AWS & Azure | Elasticsearch | AI: RAG, Evals I build .NET systems on AWS and Azure that hold up in production, including Azure OpenAI features, and I bring the same rigor to AI work: RAG measured against a test set, and AI-assisted delivery with Claude Code. 10+ years, 7 of them on AWS. Good fit if you need: - AI features inside an existing .NET product on AWS or Azure (LLM endpoints, RAG over your data, assistants) - An AI feature made measurable: test sets, retrieval metrics, an AI judge checked against human ratings - Data-heavy .NET backends: APIs, ETL, search, background processing - A secure cloud setup for AI: IAM on AWS, Entra ID and managed identities on Azure, secrets management, cost control RECENT WORK: MULTI-TENANT E-COMMERCE SAAS ON AZURE (.NET 10) - Azure OpenAI (GPT-4.1) in production, generating customer-facing content - Owned a batch processing pipeline on Azure Functions and the webhook-driven, queue-based integration with a shared platform - Idempotent, recoverable workflows over multi-tenant Cosmos DB, zero-downtime schema evolution, resilient batch writes to a third-party API - Security design for a shared platform: Entra ID between services, Entra External ID for partners, managed identities with access keys disabled, deployments without stored secrets - Cut projected non-production Azure spend by 57-69% (right-sizing, KEDA scale-to-zero, a liveness-probe redesign that ended restart loops) - Container Apps, Service Bus, Redis, Key Vault, Terraform and Bicep AI ENGINEERING - AI-assisted delivery: led the team's move to Claude Code with spec-driven development (Spec Kit), an independent reviewer agent and review-fix-re-review loops in isolated git worktrees. Built the guardrails: custom skills and commands, hooks that block unsafe actions such as pushes to main, and CLAUDE.md rules that codify the architecture. - RAG with evaluation, personal project (.NET, OpenAI, Qdrant): parent-document retrieval, metadata boosting by votes and recency, and a 100-question test set scored for retrieval (Hit@K, MRR) and by an AI judge. I keep only what measurably helps; - Agent evaluation: trained and evaluated code-focused AI agents on real Python repositories (Black, Dash) for Stellar AI. - Local RAG on AWS Bedrock with Titan embeddings and OpenSearch, personal project. EARLIER - Qiagen (7 years, AWS): architect for large-scale genomic data ingestion and Elasticsearch-based search; backends processing hundreds of millions of records, with external tools integrated into cloud-based pipelines; restructured AWS components for reliability and maintainability; containerized .NET services on EC2 with S3, IAM and monitoring; integration with the company identity service (claims, per-dataset access). - RiseTech (co-founder): architecture and development of an SMB management tool and a custom ERP/warehouse system. - Pitech+Plus: ASP.NET MVC/Web API and Xamarin projects. STACK .NET 10, C#, ASP.NET Core, AWS (EC2, S3, IAM, Bedrock, OpenSearch), Azure (Container Apps, Functions, Cosmos DB, Service Bus, Entra ID, Azure OpenAI), SQL Server, PostgreSQL, Elasticsearch, Qdrant, Python for evaluation, React with TypeScript, Terraform, Bicep, Docker. Based in Cluj-Napoca, Romania (good overlap with EU/UK, partial US). English and French. Send me where your system is today, what you need built or changed, and your timeline. I'll reply with an honest read on approach, risks and a first milestone.