Senior Software Engineer - Agentic AI - Python Expert

We are hiring senior engineers who build fast, think AI-first, and can take agentic AI from prototype to production. You will design, ship, and operate agentic systems that combine large language models (LLMs), tools/functions, planning, memory, evaluation, and multi-agent communication. You will work primarily in Python for AI services and integrate with our enterprise stack (TypeScript/Angular, .NET/C#, SQL Server, Azure), delivering trustworthy, cost-efficient, low-latency experiences in real customer workflows. What You'll Do! ·Build agentic AI applications on Azure AI Foundry: Azure OpenAI models, Prompt Flow, tools/function-calling, evaluations, vector search (Azure AI/Cognitive Search), and orchestration for multi-step reasoning and tool use. ·Design memory & grounding: implement episodic/semantic/long-term memory with vector/graph stores; architect RAG pipelines and retrieval strategies that improve factuality and reduce latency/cost. ·Integrate via Model Context Protocol (MCP) to standardize tool/skill access; design agent-to-agent communication, delegation, and event-driven workflows. ·Connect agents to Microsoft Fabric (OneLake, Lakehouse, Warehouse, Real-Time Analytics) and Dataverse entities/workflows; ensure lineage, governance, and auditability. ·Develop AI-native backend services in Python (FastAPI, asyncio) with evaluation harnesses, observability, and cost/latency/quality dashboards. ·Embed AI features into the Speria stack: TypeScript/Angular UIs, .NET/C# services, SQL Server, NServiceBus, Azure DevOps pipelines, and Ionic/Cypress where applicable. ·Use AI-augmented development tools like GitHub Copilot, Bolt, Cursor, Replit, and vibe-coding workflows to accelerate delivery, test generation, refactoring, and documentation. ·Implement safety & reliability: guardrails, red-teaming, PII protection, prompt hardening, regression tests, automated evaluations; uphold SLO/SLA excellence in production. ·Implement full cycle agentic engineering: design → model/tool selection → API & UI → deployment → monitoring → continuous improvement.