Solution Architect - Agentic AI & Data
What You Would Be Doing
Lead AI Architecture Design: Define end-to-end architecture for AI systems incorporating autonomous agents and LLM-based components, ensuring alignment with business goals.
Client Workshops & Strategy: Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints.
Multi-Agent Framework Orchestration: Design frameworks for multi-agent systems, defining roles and ensuring robust communication and fail-safes.
Integration & Scalability: Outline integration with existing enterprise ecosystems, ensuring scalability and resilience.
Leverage Prompt Engineering & RAG: Incorporate advanced prompt engineering techniques and retrieval-augmented generation (RAG) into solution design.
Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, troubleshooting high-level architectural issues.
Industry-Tailored Solutions: Customize architectural decisions to industry-specific requirements, balancing reusability with necessary adaptations.
Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into architecture standards.
Client Engagement & Travel: Work closely with client technology leaders, presenting architectural proposals and reviewing technical designs, with travel as required.
Ethical & Safe Design: Ensure ethical AI and safety considerations are embedded from the architecture stage, documenting and mitigating potential risks.
Skills Are Expected
- AI/ML Solution Architecture
- Deep Technical Knowledge
- Multi-Agent System Design
- Prompt Engineering & RAG
- AI Ethics & Responsible AI
- Cloud & Distributed Systems
- Data Management
- Leadership & Communication
- Consulting and Domain Acumen
- Problem-Solving & Innovation
- Continuous Learning