AI LLM Technology Architecture Manager

Responsibilities:

• Lead delivery of Agentic AI and GenAI solutions at the POD or workstream level.
• Support end-to-end design of AI solutions, including agent architecture, orchestration strategy, tool-calling patterns, and multi-agent coordination.
• Drive implementation of LLM selection, prompt engineering standards, RAG architecture, vector database strategy, and Responsible AI guardrails.
• Ensure code quality, architecture compliance, design review participation, sprint velocity tracking, and delivery risk management.
• Support cloud deployment across AWS, Azure, or GCP, including microservices, containerization, API gateway, CI/CD, and DevSecOps practices.
• Work with client stakeholders to understand requirements, convert business needs into technical roadmaps, and provide solution progress updates.
• Guide and mentor technical teams to deliver scalable, production-ready AI solutions with quality and speed.

Agentic AI and GenAI Leadership:

• Agent Architecture and Multi-Agent Systems, Agent Orchestration and Workflow Automation, Tool Calling and AI Integration Frameworks, Large Language Models (GPT, Claude, Gemini, Llama), Prompt Engineering, RAG, and Vector Databases, Responsible AI and AI Governance.

Solution and Delivery Leadership:

• AI Solution Architecture and Design Reviews, Agile Delivery and Program Governance, Code Quality and Engineering Best Practices, Risk, Issue, and Dependency Management, Technical Leadership and Decision Making.

Cloud and Platform Engineering:

• Azure, AWS, and GCP Cloud Platforms, Microservices and Distributed Architectures, Docker, Kubernetes, and Containerization, API Integration and Platform Engineering, CI/CD, DevSecOps, and Production Deployment.

Stakeholder and Client Management:

• Executive and Client Engagement, Business Requirements Translation, Escalation and Delivery Governance, Roadmap Planning and Progress Reporting, Cross - Functional Collaboration.

Requirements:

• Lead the end-to-end technical delivery of Agentic AI solutions.
• Drive/knowledge the implementation of Agentic AI frameworks and technologies.
• Strategic Thinking and Problem Solving, Team Leadership and People Development, Communication and Presentation Skills, Mentoring and Capability Building, Innovation and Continuous Improvement.
• Agentic AI delivery and GenAI solution implementation.
• LLMs, prompt engineering, RAG architecture, vector databases, and Responsible AI.
• AI technical delivery leadership at POD or workstream level.
• Cloud AI delivery across AWS, Azure, or GCP.
• Microservices, containerization, API gateway, CI/CD, and DevSecOps.
• Architecture compliance, code quality, sprint tracking, and risk management.
• Client stakeholder coordination and solution progress reporting.
• Team guidance and hands-on technical delivery management.
• BE, BTech, MTech.