Proven experience in designing multi-agent systems or complex process automation (minimum of 3 years)
Strong knowledge of agent frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, BMAD, or equivalent
Experience with Agentic RAG, Knowledge Graphs, and persistent memory architectures
Solid understanding of modern SDLC processes (Agile/Scrum, CI/CD, testing, code review) and their automation
Strong proficiency in Python, with the ability to prototype agents independently
Experience with Azure AI Foundry, Azure OpenAI, or equivalent cloud platforms
Ability to model business processes and translate functional requirements into scalable agent architectures
Strong analytical and problem-solving skills
Map and document SDLC and business workflows for automation and agentification (requirements, design, code, test, deploy, monitor)
Define agent typologies (autonomous, assisted, hybrid), complexity, inputs/outputs, tools, and escalation mechanisms
Design agent interface contracts (prompt templates, input/output schemas, SLAs)
Build and maintain an agent catalog ensuring reuse, scalability, and version control
Define go/no-go criteria for agent maturity levels (N0–N3)
Define Knowledge Graph strategy, including data sources, structure, and multi-hop query capabilities
Specify agent memory architecture (short-term and long-term context management)
Identify and prioritize high-impact automation opportunities (quick wins)
Define integration standards with DevOps and enterprise tools (GitHub, Jira, CI/CD, business systems)
Design Human-in-the-Loop mechanisms (approval flows, adaptive interfaces, circuit breakers)
Establish and enforce quality gates across the agent lifecycle
Ensure auditability, explainability, and traceability using observability tools (e.g., logging, OpenTelemetry)
Collaborate with security teams to define agent-specific governance and guardrails
Promote AI-native practices across development and business teams
