Lead Enterprise Agentic AI Architect; LLM Systems

We are looking for a highly experienced Agentic AI Architect to lead the design and implementation of enterprise-scale agentic AI solutions. This role will be responsible for defining architecture, guiding development teams, and ensuring scalable, secure, and high-performing AI systems that leverage LLMs, multi-agent frameworks, and enterprise integrations.

The ideal candidate combines deep technical expertise in AI/ML with strong architectural design skills and experience delivering complex, production-grade AI solutions.

• Define end-to-end architecture for agentic AI solutions, including multi-agent systems, orchestration layers, and integrations
• Design and implement scalable agent frameworks using technologies such as Lang Chain, Semantic Kernel, Auto Gen, CrewAI, etc.
• Lead development of LLM-driven systems (Azure OpenAI, OpenAI, Anthropic, etc.) with robust prompting strategies
• Architect RAG pipelines, memory systems, and tool integration layers
• Establish best practices for AI governance, security, and Responsible AI
• Design integration strategies with enterprise systems (CRM, ERP, APIs, data platforms)
• Provide technical leadership, mentorship, and code quality oversight to development teams
• Collaborate with business stakeholders to translate requirements into technical solutions
• Ensure performance, scalability, reliability, and cost optimization of AI systems
• Drive innovation by evaluating emerging AI technologies and frameworks

• 10+ years of experience in software engineering, AI/ML, or solution architecture
• Strong expertise in Python and modern backend architectures
• Hands-on experience with LLM ecosystems and agent frameworks
• Proven experience designing multi-agent systems and distributed architectures
• Deep understanding of:
  • Prompt engineering and LLM optimization
  • Embeddings and vector databases (FAISS, Pinecone, Azure AI Search, etc.)
  • Retrieval-Augmented Generation (RAG)
• Experience with cloud platforms (Azure preferred, AWS/GCP acceptable)
• Strong knowledge of API design, microservices, and event-driven architecture
• Familiarity with security, authentication (OAuth, SSO), and data governance
• Experience leading technical teams and driving architecture decisions

• Experience with Model Context Protocol (MCP) or similar integration standards
• Exposure to enterprise AI transformation programs
• Knowledge of MLOps / LLMOps frameworks
• Experience with frontend or conversational UI frameworks
• Certifications (Azure AI Engineer, Solutions Architect, etc.)