AI Solution Architect

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., 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).

Skills: Core AI & GenAI, Architecture & Design, LLM?driven workflows, agentic frameworks, API, Cloud Platforms

Experience: 15+ years, * 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.