AI Architect

Role Summary

• Architect and lead enterprise-scale AI, GenAI and Agentic AI solutions across Knowledge Management, Data Engineering, Data Management and Application Development use cases, from solution design and PoC/MVP through implementation to production.
• Design enterprise AI solutions and platforms integrating structured and unstructured data, including intelligent search, RAG, AI assistants, intelligent document processing, data intelligence and AI-enabled automation.
• Define end-to-end AI architecture covering data ingestion and engineering → data management/governance → AI/ML and GenAI → models/agents → integration → guardrails → deployment and observability, leveraging cloud and modern data/AI platforms such as AWS, Azure.
• Identify and architect AI-enabled use cases across the data and application lifecycle, including data discovery, data quality, metadata management, engineering automation, code generation/testing, application modernisation and operational support.
• Provide technical leadership across AI/ML, Data Engineering, Data Management and Cloud, translating business requirements into scalable, secure and production-ready AI solutions.

Key Experience & Skills Required

• Strong hands-on experience architecting enterprise AI, GenAI, RAG and Agentic AI solutions, including LLMs, embeddings, hybrid/vector search, grounding, multi-agent orchestration, tool integration and model routing.
• Strong AWS AI/cloud architecture experience, particularly with Amazon Bedrock, AgentCore, Knowledge Bases, Guardrails, OpenSearch and core AWS data/integration services; experience with equivalent Azure AI services and the ability to architect cloud-agnostic solutions are highly desirable.
• Experience with modern data/AI platforms such as Databricks, covering Lakehouse architecture, data engineering, data pipelines, data quality and governance, AI/ML workloads, and integration of enterprise data with GenAI and Agentic AI solutions.
• Experience architecting and implementing AI use cases across Knowledge Management, Data Engineering, Data Management and ADLC, such as intelligent search and knowledge discovery, metadata enrichment, data quality automation, engineering productivity, code/test generation and AI-driven operational automation.
• Proven experience designing production-grade, secure and governed AI solutions, including IAM/RBAC, Responsible AI, guardrails, PII/data protection, human-in-the-loop and AI/LLMOps, while balancing accuracy, scalability, performance, security, cost and maintainability.
• Experience in AI/RAG evaluation and observability, defining and monitoring evaluation metrics such as Recall, Precision, Faithfulness, Completeness, Relevance and Answer Correctness, along with LLM/agent performance, latency, token usage and operational monitoring.

Experience & Qualifications

• 10+ years of overall experience in software, data or cloud architecture, with substantial recent experience in AI/ML and GenAI solution architecture.
• Proven experience taking at least one enterprise AI/GenAI solution from architecture through production, beyond PoC or experimentation.
• AWS architecture/AI certification preferred; Azure AI Architecture certifications would be an added advantage.
• Experience working in large-scale enterprise or regulated environments with strong security, governance and auditability requirements is desirable