AI Architect

Summary:

The AI architect will define end‑to‑end agent frameworks, ensure alignment with available enterprise environment, guardrails, and partner closely with engineering, QE, security, and compliance teams.

Job Description

Business & Stakeholder Leadership

• Translate business problems into agent‑driven solution blueprints.
• Partner with senior stakeholders to identify high‑impact use cases (automation, decision support, quality, operations).
• Provide executive‑level guidance on agentic AI adoption, maturity models, and roadmaps.
• Support client conversations, RFPs, solution pitches, and thought leadership.

Architecture & Design

• Define reference architectures for agentic AI systems (single‑agent, multi‑agent, hierarchical, tool‑using agents).
• Design LLM‑driven workflows integrating reasoning, planning, memory, tools, and human‑in‑the‑loop controls.
• Architect RAG‑based and tool‑augmented agents using enterprise data sources, APIs, and workflows.
• Ensure scalability, resilience, observability, and cost optimization of agent platforms.

Governance, Risk & Guardrails

• Establish AI guardrails covering safety, bias, explainability, auditability, and regulatory compliance.
• Define agent lifecycle management (design, testing, deployment, monitoring, retirement).
• Partner with Risk, Legal, Security, and QE teams to ensure model risk management (MRM) and enterprise readiness.
• Drive standards for agent testing, validation, and certification (functional, non‑functional, and ethical).

Core AI & GenAI

• Deep expertise in LLMs, prompt engineering, and reasoning frameworks.
• Hands‑on experience with agentic frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, custom agent orchestration).
• Strong understanding of RAG, embeddings, vector databases, and knowledge grounding.
• Experience with fine‑tuning techniques (LoRA / QLoRA) and evaluation strategies.

Architecture & Engineering

• Strong background in distributed systems, APIs, microservices, and cloud‑native architectures.
• Proficiency in Python and familiarity with enterprise integration patterns.
• Experience with cloud platforms (Azure, AWS, GCP) and secure enterprise deployments.
• Knowledge of observability, monitoring, and cost management for AI systems.