Packaged/SaaS Application Engineer

Project Role : Packaged/SaaS Application Engineer
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : .Net Full Stack Development
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
We re building agentic, production-grade AI apps that go from idea to working solution in hours, not weeks. You ll ship multi-agent workflows, secure enterprise integrations, and real user-facing copilots on Microsoft s AI platform.

Roles & Responsibilities:
Design and implement agent + workflow architectures (single/multi-agent, routing, tool calling, memory/state).
Build rapid prototypes, then harden to production (security, reliability, monitoring, cost).
Implement enterprise integrations (APIs, data sources, connectors, functions) and ship end-to-end experiences (Teams/web).
Create and maintain evaluation harnesses (quality, regressions, safety, prompt/tool tests).
Build with modern AI-assisted engineering workflows (agentic coding, PR automation, tests).
Agentic AI applications: tool-using agents that plan, act, and complete workflows (not just chat).
RAG + enterprise grounding using Azure AI Search / Microsoft data sources (SharePoint/Fabric/etc. depending on scenario).
Copilots/Agents via Copilot Studio (low code) and/or pro-code stacks (Foundry + SDKs).
Observability + evaluation: tracing, metrics, and safe operations for agents.

Professional & Technical Skills:
- Must To Have Skills:
Strong software engineering in C#/.NET and/or Python API-first and cloud-native patterns.
Hands-on with Azure OpenAI / Azure AI app development and tool/function calling patterns.
Experience building agentic systems using one or more:
Microsoft Agent Framework (or Semantic Kernel agent framework)
Semantic Kernel / AutoGen patterns (multi-agent orchestration)
Production mindset: testing, CI/CD, telemetry, incident readiness.
Azure OpenAI, Azure AI Search, Azure Functions, Cosmos DB / Azure SQL, Microsoft Fabric
Copilot Studio (agents, connectors, plugins, actions)
Secure-by-design: identity, secrets, and least-privilege access patterns (e.g., Entra-backed auth).
- Good To Have Skills:
Azure AI Foundry Agents experience (agent service + SDK), including tracing/monitoring.
Copilot Studio: generative answers/actions, plugins, connectors, multi-channel publishing.
MCP (Model Context Protocol) exposure for tool/server interoperability.
Practical use of GitHub Copilot Agent Mode to accelerate multi-step dev loops.
Familiarity with Claude Code (agentic coding in terminal/IDE) as part of your personal dev acceleration toolkit.