Job Description :

As s AI Solution Architect with deep expertise in agentic AI to design and deliver enterprise-grade AI, GenAI, and automation solutions that embed into Nokia's IT and Digital Office (DO) processes. 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.

Your Responsibilities :

- 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.

- Lead technical design reviews, architecture boards, and proof-of-concept evaluations.

- Set coding, model, prompt, agent, and AgentOps standards; champion responsible AI, evaluation rigor, and observability.

- Define and evolve the GenAI and agentic platform stack model gateways, vector stores, orchestration frameworks, tool registries, agent runtime, evaluation pipelines, tracing, and cost controls.

- 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.

Your Skills and Experience :

- 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.