AI Architect

Responsibilities

• Architectural Design & Implementation: Design, document, and oversee the implementation of Client target AI architecture, focusing on the KDB layer, product-agnostic semantic layer, reusable application (RApp) layer, and agentic components, based on strategic recommendations.
• Execution & Delivery: Drive the technical execution of the AI architecture roadmap. Translate high-level designs into detailed technical specifications, work plans, and deliverables. Ensure timely and high-quality delivery of foundational AI components and services.
• Agent Ecosystem Development: Lead the design and development of a scalable, reusable, and product-agnostic agentic ecosystem built upon the KDB layer. Define agent interaction patterns, communication protocols, and integration strategies.
• Knowledge & Data Integration: Architect solutions for integrating diverse knowledge sources (including source code, API specifications, and structured data) into a unified, semantically rich layer. Design automated processes for knowledge extraction, validation, and lifecycle management, moving away from SME-dependent and ad-hoc approaches.
• Experimentation & Prototyping: Foster a culture of experimentation by rapidly prototyping new approaches, evaluating different technologies (e.g., LLMs, vector databases, agent frameworks), and iterating based on results and feedback. Define metrics and processes for evaluating architectural choices and component performance.
• Technical Leadership & Collaboration: Provide technical leadership and guidance to engineering and data science teams involved in building AI solutions. Collaborate closely with product managers, data scientists, software engineers (backend and frontend), and other stakeholders to ensure architectural alignment and successful integration.
• Bridging Strategy and Execution: Act as the key technical liaison, effectively communicating complex architectural concepts to both technical and non-technical audiences. Ensure that implementation details align with the overarching strategic vision outlined in the architectural recommendations.
• Risk Management & Scalability: Proactively identify technical risks and challenges related to scalability, performance, security, and maintainability. Design solutions that mitigate these risks and ensure the long-term viability of the AI architecture.

Qualifications

• Experience: Proven track record in designing and implementing complex, large-scale software and AI/ML systems.
• AI/ML Expertise: Deep understanding of AI/ML concepts, algorithms, and techniques. Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch), NLP, and LLMs.
• Architecture Skills: Strong software architecture skills, including microservices, APIs, data modeling, distributed systems, and cloud-native patterns. Experience designing for scalability, reliability, and performance.
• Agent Systems: Demonstrable experience in designing, building, and deploying agent-based systems or multi-agent ecosystems.
• Data Engineering: Solid understanding of data engineering principles, data pipelines, databases (SQL/NoSQL), and data warehousing concepts.
• Programming: Proficiency in relevant programming languages (e.g., Python, Java, Scala).
• Execution Focus: Strong bias for action, ability to drive projects to completion, and experience translating strategy into concrete technical execution.