Senior Enterprise AI Architect

Define and drive the enterprise-wide AI architecture strategy aligned with business and digital transformation goals.
Design and implement scalable Agentic AI systems, including autonomous and semi-autonomous AI agents capable of planning, reasoning, and executing complex workflows.
Architect and manage Multi-Agent Orchestration frameworks, enabling coordination between multiple AI agents with defined roles such as planner, retriever, executor, and validator.
Lead LLM integration initiatives, including integration of foundation models (open-source and enterprise-grade) with internal systems via APIs and secure gateways.
Design and implement enterprise-grade RAG (Retrieval-Augmented Generation) frameworks, including vector databases, embeddings, semantic search, and knowledge grounding strategies.
Define and implement Model Context Protocol (MCP) standards to enable structured and secure communication between AI agents, enterprise tools, and data sources.
Establish and enforce AI Governance frameworks, covering responsible AI, model explainability, bias mitigation, risk management, compliance, auditability, and security controls.
Architect AI solutions within multi-cloud environments (AWS, Azure, GCP) leveraging native AI/ML services, containerization, Kubernetes, serverless computing, and hybrid cloud strategies.
Implement MLOps / LLMOps pipelines including model lifecycle management, monitoring, prompt versioning, performance tracking, and drift detection.
Collaborate with business stakeholders, engineering teams, and leadership to translate AI use cases into scalable, secure, and production-ready solutions.
Evaluate emerging AI technologies and recommend adoption strategies aligned with enterprise standards.
Required Skills & Experience
IT experience with 5+ years in AI/ML architecture roles.
Strong experience in Enterprise AI Architecture design and implementation.
Hands-on experience with Agentic AI and Multi-Agent systems.
Proven expertise in LLM integration and RAG frameworks.
Experience implementing Model Context Protocol (MCP) or similar agent communication standards.
Strong understanding of AI Governance, Responsible AI, and regulatory compliance frameworks.
Extensive experience across AWS, Azure, and/or GCP AI ecosystems.
Experience with containerization (Docker, Kubernetes) and API-driven architectures.
Strong knowledge of vector databases, embeddings, and semantic search technologies.
Skills: llm,rag,agent ai