AI Architect

Role Summary

• Architect and lead enterprise-scale AI, GenAI, and Agentic AI initiatives across Knowledge Management, Data Engineering, Data Management, and Application Development use cases—covering everything from solution design and PoC/MVP to implementation and production.
• Build enterprise AI solutions and platforms that connect structured and unstructured data, including intelligent search, RAG, AI assistants, intelligent document processing, data intelligence, and AI-enabled automation.
• Own end-to-end AI architecture across the full lifecycle: data ingestion and engineering → data management/governance → AI/ML and GenAI → models/agents → integration → guardrails → deployment and observability, using cloud and modern data/AI platforms such as AWS and Azure.
• Discover, design, 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 modernization, and operational support.
• Provide technical leadership across AI/ML, Data Engineering, Data Management, and Cloud—translating business requirements into secure, scalable, production-ready AI solutions.

Key Experience & Skills Required

• Strong hands-on expertise in 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, especially with Amazon Bedrock, AgentCore, Knowledge Bases, Guardrails, OpenSearch, and core AWS data/integration services; comparable Azure AI services experience and the ability to architect cloud-agnostic solutions are highly desirable.
• Experience with modern data/AI platforms such as Databricks, including Lakehouse architecture, data engineering, data pipelines, data quality and governance, AI/ML workloads, and integrating enterprise data with GenAI and Agentic AI solutions.
• Proven ability to architect and implement AI use cases across Knowledge Management, Data Engineering, Data Management and ADLC, including intelligent search and knowledge discovery, metadata enrichment, data quality automation, engineering productivity, code/test generation, and AI-driven operational automation.
• Demonstrated 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 with AI/RAG evaluation and observability—defining and tracking evaluation metrics such as Recall, Precision, Faithfulness, Completeness, Relevance, and Answer Correctness, plus LLM/agent performance, latency, token usage, and operational monitoring.

Experience & Qualifications

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