We are seeking 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 secure, scalable, and high-performing AI systems that leverage Large Language Models (LLMs), multi-agent frameworks, and enterprise integrations.

The ideal candidate combines deep expertise in AI/ML, software architecture, and enterprise solution design, with a proven track record of delivering complex, production-grade AI systems.

AI Solution Architecture

• Define end-to-end architecture for enterprise Agentic AI solutions.
• Design multi-agent systems, orchestration layers, and enterprise integrations.
• Establish architectural standards, design patterns, and best practices.

Agent Framework Development

• Design and implement scalable agent frameworks using technologies such as:
  • LangChain
  • Semantic Kernel
  • AutoGen
  • CrewAI
  • Other emerging agent orchestration frameworks
• Build robust and maintainable AI architectures capable of enterprise-scale deployment.

LLM & AI Engineering

• Lead the development of LLM-driven solutions using:
  • Azure OpenAI
  • OpenAI
  • Anthropic
  • Similar foundation model platforms
• Develop effective prompting strategies and optimize model interactions.
• Design solutions that maximize accuracy, performance, and reliability.

RAG & Knowledge Systems

• Architect and implement:
  • Retrieval-Augmented Generation (RAG) pipelines
  • Knowledge retrieval architectures
  • Memory management systems
  • Tool integration frameworks
• Enable AI agents to effectively access and utilize enterprise knowledge.

Governance, Security & Responsible AI

• Establish AI governance frameworks and standards.
• Implement Responsible AI principles and controls.
• Ensure compliance with security, privacy, and data governance requirements.
• Define risk management practices for enterprise AI solutions.

Enterprise Integration

• Design integration strategies with enterprise applications and platforms, including:
  • CRM systems
  • ERP platforms
  • APIs
  • Data platforms
  • Third-party services
• Develop scalable and secure enterprise integration patterns.

Technical Leadership

• Provide technical leadership and mentorship to engineering teams.
• Guide architecture decisions and implementation approaches.
• Review code quality, scalability, and overall solution design.
• Drive engineering excellence and innovation across teams.

Stakeholder Collaboration

• Partner with business and technology stakeholders to translate business requirements into technical solutions.
• Communicate architectural strategies and implementation roadmaps to executive leadership.

Performance & Innovation

• Ensure AI systems meet targets for:
  • Performance
  • Scalability
  • Reliability
  • Cost optimization
• Continuously evaluate emerging AI technologies, tools, and frameworks to drive innovation.