Agentic AI Architect

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)
• 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 exp

10+ years' experience in distributed systems/ML

Minimum Requirement

Desired

Experience

• 10+ 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

1. AWS Certified Machine Learning - Specialty
2. Azure AI Engineer Associate