AI Solution Architect

You will architect multi-agent systems, autonomous workflows, and human-in-the-loop copilots using modern agentic frameworks — translating business problems into robust, scalable, and responsible AI architectures. You will be the senior technical authority bridging business stakeholders, data scientists, platform engineers, and IT architecture functions.

• Architect agentic AI systems — single-agent, multi-agent, and orchestrator/worker patterns — for autonomous and semi-autonomous DO and business workflows.
• Design solutions using modern agentic frameworks such as Microsoft Agent Framework (MAF), Semantic Kernel, LangChain / LangGraph, AutoGen, Google Agent SDK
• Define agent capabilities, tool/function calling contracts, memory strategies (short-term, long-term, episodic), planning and reflection loops, and inter-agent communication protocols (e.g., MCP, A2A).
• Own the end-to-end architecture: data ingestion, retrieval (RAG, GraphRAG, hybrid search), model selection, agent orchestration, integration, and serving.
• Produce high-level and detailed design artifacts: solution blueprints, agent topology diagrams, sequence flows, API/tool contracts, and non-functional requirement specs.
• Drive build-vs-buy decisions and evaluate vendor/hyperscaler offerings against Nokia standards.
• Ensure solutions align with Nokia's enterprise architecture, security, identity, data-protection, and integration standards.
• Define reusable architectural patterns and reference designs for agent-based IT/DO solutions.
• Provide hands-on technical leadership across multiple parallel initiatives; mentor GenAI engineers, and platform developers on agentic patterns and frameworks.
• Set coding, model, prompt, agent, and AgentOps standards; champion responsible AI, evaluation rigor, and observability.
• Establish AgentOps / LLMOps practices: CI/CD for agents, prompts, and tools; A/B testing; trajectory and trace analysis; drift and quality monitoring; retraining; and decommissioning.

• 10+ years in software/solution architecture, with 5+ years architecting AI/ML or data-intensive systems and recent hands-on experience designing GenAI and agentic AI solutions and Deep, hands-on expertise with at least two agentic frameworks — Microsoft Agent Framework (MAF), Semantic Kernel, LangChain / LangGraph, AutoGen, CrewAI, or LlamaIndex agents.
• Strong understanding of agent design patterns: ReAct, Plan-and-Execute, reflection, tree-of-thought, multi-agent collaboration, orchestrator/worker, and supervisor-critic loops.
• Proficiency in tool/function calling, structured outputs, MCP, and agent-to-agent communication patterns.
• Deep expertise in Python and /or C# and with Strong experience with cloud AI platforms — Azure AI / Azure OpenAI / Azure AI Foundry preferred, plus GCP Vertex AI.
• Proven track record designing production-grade RAG, agentic, and fine-tuning solutions, including vector databases (Azure AI Search, Pinecone, Weaviate, pgvector).
• Solid foundation in distributed systems, microservices, APIs, event-driven architecture, containers (Docker/Kubernetes), and CI/CD.
• Strong grasp of MLOps / LLMOps / AgentOps, evaluation frameworks, and AI observability.

Nice-To-Have:

• Excellent communication skills — able to influence both engineering teams and executive stakeholders.ot/cause analysis in more complex problems. Can develop and implement recommendations. Managerial/Supervisory: Direct supervisory responsibilities for people.
• Typically first level (and lowest level) of solid line management. Carries out variety of complex activities according to plan within broader area of responsibility, analyses problems. Decision-making typically according to established solutions.