Agentic Architect

About the Role

We are seeking an experienced AI Agentic Solutions Architect to design and lead enterprise-scale agentic AI platforms. This role is focused on architecting advanced multi-agent systems, LLMOps platforms, RAG solutions, and Responsible AI frameworks while establishing engineering standards for scalable, secure, and production-ready AI applications.

Key Responsibilities

• Define enterprise architecture standards for agentic AI, including agent design patterns, deployment topology, evaluation strategies, and Responsible AI governance.
  


• Design and implement advanced multi-agent systems with supervisor hierarchies, agent delegation, shared memory, agent-to-agent communication, and orchestration workflows.
  


• Architect scalable Retrieval-Augmented Generation (RAG) platforms using Azure AI Search, vector databases, GraphRAG, and Knowledge Graph technologies.
  


• Build and optimize LLMOps platforms using PromptFlow, LangSmith, model evaluation frameworks, AI observability, and cost optimization strategies.
  


• Design Responsible AI frameworks, including guardrails, content safety, bias detection, audit logging, and governance controls.
  


• Develop evaluation frameworks with offline benchmarks, online monitoring, A/B testing, and human feedback mechanisms.
  


• Collaborate with Data Engineering teams to design data pipelines, knowledge bases, and fine-tuning datasets for AI solutions.
  


• Drive architecture reviews, establish engineering best practices, and mentor development teams on enterprise AI standards.
  


• Ensure production resilience through model routing, fallback strategies, checkpointing, token optimization, and multi-provider LLM deployments.
  

Key Skills & Qualifications

• 10+ years of software engineering experience with strong expertise in Python and Java or .NET.
  


• Deep architectural expertise in LangChain, LangGraph, and enterprise AI application development.
  


• Hands-on experience with Semantic Kernel, CrewAI, AutoGen, and LlamaIndex.
  


• Strong knowledge of Azure AI Search, Vector Databases, GraphRAG, Neo4j, and Knowledge Graph architectures.
  


• Experience building enterprise RAG platforms and AI knowledge retrieval systems.
  


• Expertise in PromptFlow, LangSmith, AI observability, model evaluation, and LLMOps best practices.
  


• Strong understanding of Responsible AI principles, including fairness, bias detection, explainability, and governance.
  


• Experience with model routing, token budgeting, rate-limit handling, multi-provider LLM architectures, and production AI operations.
  


• Experience deploying scalable AI solutions on AWS or Azure (cloud certification preferred).
  


• Excellent technical leadership, architecture review, stakeholder management, and mentoring skills.
  

Preferred Skills

• Experience with Model Context Protocol (MCP) and AI tool ecosystem governance.
  


• Knowledge of prompt injection prevention, secure agent execution, and AI security best practices.
  


• Experience with fine-tuning techniques including LoRA, RLHF, and DPO.
  


• Familiarity with MLflow for experiment tracking and model lifecycle management.
  


• Experience using AI-assisted development tools such as Claude Code and Cursor.
  


• Strong understanding of Architecture Decision Records (ADRs) and enterprise design governance.
  
  Apply by sending your CV to [email protected]