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.