The impact you’ll make as a Lead AI Back-End Engineer
• Set the technical architecture for our agent powered platform, choosing the right mix of LLMs, retrieval technologies, agent frameworks, and microservice patterns to meet reliability, cost, quality, and latency targets.
• Drive architecture for high throughput APIs built with .NET, C#, Python 3.11+, FastAPI async, SQLModel, and Semantic Kernel, from design documents through production rollout.
• Lead multi agent orchestration across handoff, sequential, parallel, and supervisor patterns, combining knowledge grounded and tool calling agents across OpenAI GPT 5.6 Sol, Terra and Luna, Google Gemini 3.7 Flash, Anthropic Claude Sonnet 5 and Opus 5, xAI Grok 4.6, and future model providers.
• Guide teams implementing Retrieval Augmented Generation on Azure AI Search, pgvector, Chroma, and equivalent vector and hybrid retrieval platforms, ensuring index quality, filtering, evaluation, and safety controls.
• Own end to end CI/CD pipelines using Bitbucket Pipelines, Jenkins, or similar platforms, including linting, type checking, security scanning, testing, containerization, and deployment.
• Mentor and hire engineers and adopt AI coding agents such as Cursor and Claude Code as engineering force multipliers while maintaining strong code quality, security, and review standards.
• Champion observability and FinOps for LLM workloads using structured JSON logging, OpenTelemetry tracing, Langfuse, evaluation frameworks, and cost dashboards, keeping latency and cost per request within defined SLOs.
• Partner with Product and Security to translate business goals, compliance requirements, emerging AI capabilities, and user feedback into a pragmatic technical roadmap.