AI Architect

Description:

Job Title: AI Architect

Experience level: 10 years

Job Summary

We are seeking an experienced AI Architect to design and lead enterprise-scale AI ML and Generative AI solutions built on AWS and Azure as the core AI foundation with Microsoft Copilot as the primary user experience layer. The role is responsible for designing the end-to-end AI solution architecture ensuring alignment with enterprise systems scalability and governance standards while integrating AI into the broader IT landscape. It requires deep expertise in RAG (Retrieval-Augmented Generation) and Agentic AI architectures on cloud-native platforms enabling intelligent scalable and production-ready AI systems after understanding the current product architecture. The candidate should also be able to conduct POCs to demonstrate proof of design considerations.

Key Responsibilities

AI ML & GenAI Architecture

• Design and define end-to-end AI solution architectures covering data ingestion model training deployment monitoring and governance ensuring alignment with enterprise systems and IT landscape while meeting scalability and governance standards.

• Design scalable cloud-native AI platforms on AWS and Azure.

• Architect solutions for both batch and real-time inference workloads.

RAG (Retrieval-Augmented Generation)

• Architect and implement RAG pipelines using structured and unstructured enterprise data.

• Design ingestion chunking embedding and retrieval strategies for RAG systems.

• Integrate vector databases (e.g. Pinecone FAISS Milvus Azure AI Search Amazon OpenSearch).

• Ensure relevance freshness observability and security of RAG-based AI systems.

Agentic AI & Autonomous Systems

• Design Agentic AI architectures enabling autonomous decision-making and task execution.

• Orchestrate multi-agent systems using tools memory and reasoning workflows.

• Implement guardrails human-in-the-loop controls and observability for agent-based systems.

• Enable enterprise use cases such as AI assistants Microsoft Copilot-integrated workflows task automation and decision intelligence.

MLOps & LLMOps

• Define and implement MLOps / LLMOps frameworks for CI/CD versioning monitoring and drift detection.

• Enable experimentation evaluation and governance of ML models and LLM-based systems.

• Ensure compliance with security privacy and responsible AI guidelines.

Cloud & Platform Engineering

• Architect AI solutions on AWS and Azure as the primary cloud platforms integrating Microsoft Copilot as the enterprise user experience layer.

• Integrate AI platforms with enterprise applications APIs and data sources.

• Design highly available secure and scalable AI systems.

Leadership & Collaboration

• Serve as a technical thought leader for AI GenAI and data platforms.

• Mentor data scientists ML engineers and data engineers.

• Collaborate with business and product teams to translate requirements into AI-driven solutions.

• Evaluate emerging AI technologies and guide strategic adoption.