We are looking for a Principal GenAI / Agentic AI Architect to define the technical vision and enterprise architecture for large-scale AI-powered platforms and intelligent applications.

The role will focus on enterprise AI architecture, technology strategy, platform standards, AI governance, agentic systems, and large-scale adoption of Generative AI.

The candidate will provide technical direction across multiple engineering teams and work with senior technology stakeholders to establish scalable and responsible AI capabilities.

This is an architecture and technology leadership role with emphasis on enterprise-wide impact rather than individual feature development.

Key Responsibilities :

Enterprise AI Strategy :

- Define the technical strategy and architecture roadmap for enterprise GenAI adoption.

- Establish reference architectures for LLM-powered applications and Agentic AI systems.

- Identify opportunities to apply AI across enterprise processes and technology platforms.

- Evaluate emerging AI technologies, models, frameworks, and infrastructure.

- Define technology standards and architectural principles for AI engineering.

Agentic AI Architecture :

- Architect enterprise-scale autonomous and semi-autonomous agent ecosystems.

- Define patterns for multi-agent collaboration, orchestration, tool use, memory, planning, and workflow execution.

- Establish architectural standards for agent reliability, observability, security, and governance.

- Define reusable agent platforms and enterprise AI services.

- Design architectures that balance autonomous AI behavior with deterministic enterprise controls.

LLM & AI Platform Architecture :

- Define enterprise architecture for LLM integration, model routing, inference, and AI services.

- Establish strategies for model selection, evaluation, versioning, scalability, and lifecycle management.

- Define architecture for RAG, vector search, knowledge systems, and enterprise data integration.

- Establish reusable AI platform capabilities for engineering teams.

- Evaluate hosted and open-source models based on enterprise requirements.

AI Governance & Responsible AI :

- Define enterprise standards for AI security, privacy, compliance, and responsible use.

- Establish governance frameworks for AI applications and agentic systems.

- Define controls for hallucination, prompt injection, data leakage, inappropriate model behavior, and unauthorized tool execution.

- Establish AI evaluation and quality frameworks.

- Partner with security, risk, legal, and compliance teams on AI governance initiatives.

Cloud & Platform Architecture :

- Define scalable cloud architecture for AI workloads across Azure, AWS, and GCP.

- Establish standards for Kubernetes, container platforms, APIs, serverless services, and AI infrastructure.

- Define architecture for production AI deployment, observability, scalability, and disaster recovery.

- Drive adoption of DevSecOps, CI/CD, Infrastructure as Code, and MLOps/LLMOps practices.

- Establish enterprise patterns for integrating AI services with existing technology ecosystems.

Architecture Governance :

- Lead architecture reviews for strategic AI programs.

- Define technology standards and reference implementations.

- Review high-impact AI solution designs and technical decisions.

- Identify architectural risks and define mitigation strategies.

- Drive technology modernization and platform consolidation initiatives.

Technical Leadership :

- Provide technical direction to senior engineers, architects, and AI specialists.

- Mentor technical leaders and establish engineering excellence practices.

- Influence technology strategy across multiple engineering organizations.

- Collaborate with CTO, architecture, product, security, data, and engineering leadership.

- Communicate complex AI architecture decisions and roadmaps to senior stakeholders.

Required Skills :

- Deep expertise in Generative AI and LLM application architecture.

- Extensive Agentic AI / multi-agent architecture experience.

- Enterprise RAG and knowledge architecture.

- LLM orchestration and model integration.

- AI platform architecture.

- Vector databases and semantic search.

- Python and/or JavaScript/TypeScript.

- Azure OpenAI and enterprise AI platforms.

- LangChain, LlamaIndex, Semantic Kernel, or equivalent.

- Cloud architecture across Azure, AWS, and/or GCP.

- Kubernetes and containerized AI workloads.

- API and distributed systems architecture.

- AI security, governance, and Responsible AI.

Preferred Skills :

- LLMOps / MLOps.

- Model fine-tuning, quantization, and inference optimization.

- AI evaluation frameworks.

- Event-driven and real-time AI architectures.

- Knowledge graphs and advanced retrieval architectures.

- Enterprise AI platform development.

- Experience with hyperscale or large global enterprise environments.

Educational Qualification :

- Bachelor's or Master's degree in Computer Science, Engineering, AI, or a related discipline.

Candidate Profile :

- Extensive experience in enterprise software and AI architecture.

- Proven ability to define AI technology strategy across multiple teams.

- Deep understanding of LLMs, RAG, Agentic AI, cloud architecture, and distributed systems.

- Strong ability to balance innovation with security, reliability, scalability, and governance.

- Excellent architecture, communication, stakeholder management, and technical leadership skills.

- Ability to drive enterprise-scale AI transformation initiatives.