Agentic AI Architect

Job description

1. Job Purpose The Agentic AI Architect is responsible for defining and delivering the architecture for next-generation AI systems leveraging Large Language Models (LLMs), autonomous agents, and multi-agent orchestration frameworks to enable intelligent automation and advanced digital capabilities. This role focuses on designing scalable, secure, and production-ready AI platforms that support intelligent decision systems, conversational interfaces, automation workflows, and data-driven operational insights. The Architect will collaborate closely with AI engineers, data engineers, platform teams, and product stakeholders to build enterprise-grade agentic AI systems aligned with organizational technology strategy, governance standards, and security policies. 1. Key Accountabilities Strategic Activities • Define enterprise architecture for agentic AI platforms, including multi-agent systems, orchestration frameworks, and LLM-driven applications. • Design secure Function Calling interfaces and Tool Definition schemas to enable agents to interact with legacy systems, SQL databases, and enterprise CRMs. • Architect Human-in-the-loop checkpoints and state-management protocols to ensure autonomous actions remain within defined operational guardrails. • Drive adoption of Generative AI and autonomous agent systems across digital and operational platforms. • Establish architecture standards for LLM pipelines, prompt engineering, evaluation frameworks, vector search, and Retrieval Augmented Generation (RAG). • Design scalable AI inference architectures and microservices optimized for latency, cost efficiency, and reliability. • Define governance frameworks ensuring responsible AI usage, security, explainability, and regulatory compliance. • Contribute to the AI technology roadmap, including evaluation of new AI platforms, frameworks, and vendor solutions. • Monitor emerging AI technologies and evaluate their potential impact and opportunities for the organization. • Balance rapid innovation and experimentation with enterprise-grade reliability and operational stability. Solution Architecture & Technical Leadership • Architect end-to-end agentic AI systems including LLM orchestration layers, agent coordination mechanisms, and intelligent workflow automation. • Design architectures integrating LLM inference services, vector databases, APIs, and enterprise data platforms. • Define architectural patterns for multi-agent coordination, memory management, tool usage, and reasoning workflows. • Develop reusable architectural frameworks and design patterns to accelerate AI solution development. • Evaluate architecture alternatives and define trade-offs between performance, cost, scalability, and security. • Provide technical guidance to engineering teams implementing AI-driven solutions. • Ensure architectural alignment with enterprise architecture standards and cloud strategy. Research & Emerging Technology Monitoring • Track advancements in LLMs, agent frameworks, orchestration tools, reasoning engines, and AI infrastructure. • Conduct research and experimentation to evaluate emerging AI technologies and frameworks. • Develop prototypes and proof-of-concepts to validate architectural approaches. • Document research findings and architectural guidance for internal knowledge sharing. • Participate in AI technology communities and industry forums to remain current with evolving AI trends. Systems & Software Design • Design software components supporting agent orchestration, AI services, and inference pipelines. • Produce architecture documentation covering system components, interfaces, and integration patterns. • Develop multiple architectural views addressing both functional and non-functional requirements. • Lead architecture and design reviews to ensure adherence to enterprise standards. AI Platform Engineering & Integration • Define and implement LLMOps / MLOps practices supporting model evaluation, monitoring, experimentation, and deployment. • Establish observability frameworks for monitoring model performance, latency, reliability, and cost efficiency. • Integrate AI services with enterprise applications through APIs, microservices, and data pipelines. • Ensure production readiness of AI platforms through testing, monitoring, and performance optimization. Team Leadership & Collaboration • Provide architectural leadership to AI engineers, LLM engineers, and data engineers. • Mentor engineering teams on AI architecture patterns, best practices, and design principles. • Collaborate with product and business teams to translate requirements into scalable AI solutions. • Support capability building and knowledge sharing across AI and engineering teams. • Participate in recruitment and development of AI engineering talent. 1. Skills Required for the Role AI & Machine Learning • Strong expertise in machine learning, generative AI, and large language models • Experience designing LLM-based applications and agentic AI systems • Hands-on experience with LangGraph, CrewAI, Autogen, or Semantic Kernel for multi-agent coordination. • Experience in designing State Management and persistent memory systems (e.g., Zep, Mem0) for long-running autonomous tasks. • Knowledge of prompt engineering, embeddings, vector databases, and RAG architectures • Familiarity with AI orchestration frameworks and autonomous workflow design • Experience implementing AI evaluation and monitoring frameworks Programming & Engineering • Strong programming skills in Python • Experience with ML frameworks such as PyTorch, TensorFlow, or Keras • Experience with data processing libraries (NumPy, Pandas, Scikit-learn) • Ability to design scalable microservices and distributed systems • Experience developing APIs and integration services Cloud & AI Infrastructure • Experience deploying AI solutions on cloud platforms (AWS, Azure, or GCP) • Familiarity with containerization and orchestration (Docker, Kubernetes) • Know