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.
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.
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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.
Any other additional responsibility could be assigned to the role holder from time to time as a standalone project or regular work. The same would be suitably represented in the Primary responsibilities and agreed between the incumbent, reporting officer and HR.
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)
Knowledge of vector databases, data pipelines, and AI infrastructure
Experience with LLMOps / MLOps platforms
Architecture & System Design
Expertise in distributed systems architecture
Strong understanding of scalability, reliability, and performance engineering
Ability to design enterprise-grade AI platforms and frameworks
Leadership & Communication
Strong technical leadership and mentoring capabilities
Excellent analytical and problem-solving skills
Ability to communicate complex AI concepts to both technical and non-technical stakeholders
Strong documentation and architecture communication skills
D. Educational and Experience Requirements
Minimum Education Requirements
Master's degree in computer science, AI/ML, or related field OR bachelor's degree
15+ years of total work ex
10+ years' experience in distributed systems/ML
Minimum Requirement
Desired
Experience
8+ years in software architecture or ML engineering
3+ years hands-on experience with LLMs and generative AI
Proven track record designing production AI systems at scale
Experience with agent frameworks (LangGraph, CrewAI, Autogen, etc.)
10+ years in AI/ML systems architecture
Experience in highly regulated industries (finance, healthcare, aviation)
Prior experience with autonomous systems or robotics
Published research or open-source contributions in agentic AI
Certifications
AWS Certified Machine Learning - Specialty
Azure AI Engineer Associate
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