Gen AI Architect

Role Overview
We are seeking experienced AI Architects to join a centralized AI Center of Excellence (CoE), responsible for driving enterprise-wide AI transformation. This role focuses on embedding agentic AI into delivery models and the end-to-end SDLC, while defining governance, standards, and scalable AI frameworks in a highly regulated environment. The architect will act as a strategic authority and hands-on leader, ensuring consistent, secure, and high-quality AI implementations across client-facing and internal platforms.
Key Responsibilities
AI-Driven SDLC Transformation
Design AI-first delivery models across requirements, development, testing, and deployment. Integrate AI agents into DevOps/CI-CD pipelines and define measurable productivity KPIs (cycle time, defect reduction, velocity).
Agentic AI Architecture
Design and implement multi-agent systems using frameworks like LangChain, AutoGen, and CrewAI.
Define agent patterns (planner-executor, tool-using agents, critic loops) and ensure observability, explainability, and guardrails.
AI Platform & Framework Development
Build reusable accelerators, playbooks, and reference architectures. Establish standards for prompt engineering, RAG, model selection, and evaluation. Enable self-service AI platforms using cloud ecosystems like Amazon Web Services and Microsoft Azure.
Preferred Qualifications
Financial services domain experience
Experience building internal developer platforms (IDPs)
Cloud certifications (AWS/Azure/GCP)
Exposure to spec-driven AI development and AI playbooks
Required Qualifications
12+ years in software engineering/architecture; 5+ years in AI/ML
Strong hands-on experience with GenAI, LLMs, and agentic AI systems
Expertise in RAG, embeddings, prompt engineering, and evaluation frameworks
Experience with distributed systems, APIs, microservices, and Kubernetes
Familiarity with MLOps/LLMOps pipelines and cloud AI platforms
Experience working in regulated environments (financial services preferred)
Strong understanding of AI governance, model risk, and data privacy

Work arrangement
No