AI Solution Architect

Agentic AI Solution Architect

Note: This is a HYBRID position requiring 2 in-office days at local Deloitte office per week. Travel costs are not reimbursable. Candidates with sub vendors cannot be considered.

Sr Agentic AI Solution Architect
Our client is currently undergoing a multiyear transformation of its digital footprint in the Enterprise Service Strategy domain. We are seeking an experienced Solution Architect who will lead the strategy and architectural design for a modern Agentic AI based Service Strategy and partner with development teams on the solution implementation, etc. The Agentic AI Solutions Architect is responsible for designing, governing, and scaling autonomous (“agentic”) AI applications and Agents across the enterprise. This role blends deep technical expertise with architectural leadership and is accountable for enabling safe, reliable, and value driven AI adoption. The architect defines patterns, guardrails, and reference architectures that accelerate the development of AI-driven workflows, agent orchestration, and tool-enabled reasoning systems. This role is highly cross-functional, partnering with engineering, data, security, product, and business teams to identify high-impact use cases, assess automation readiness, and deliver enterprise-grade AI solutions. The expectation would be to understand the contact center landscape at Jones, understand the underlying data, understand the solve in terms of enabling self-service agents and leveraging the jones ecosystem to deliver on those agents.

Technical Experience Areas
Cloud Native Architecture
Azure Cloud Platform Expertise
Strong proficiency with agent frameworks, and planner/executor patterns.
Solid understanding of LLM architectures, embeddings, vector search, RAG, prompt engineering, and safe-output techniques.
Experience integrating AI systems with REST/GraphQL APIs, event-driven architectures, microservices, and cloud platforms.
Familiarity with Databricks, Unity Catalog, and enterprise data governance best practices.
Hands-on engineering experience with Python, containerization (Docker), and modern CI/CD pipelines.
Deep understanding of the Model Context Protocol (MCP) as the enterprise standard for tool integration and context exchange.
Ability to architect agent workflows using MCP hosts, clients, and servers, including multi-server and hybrid patterns.
Experience consuming existing MCP servers and designing custom MCP servers to expose enterprise tools and business logic.
Understand how a cognigy bot with NiCE can be leveraged as part of this ecosystem.
Understand how to act on the data insights coming out of the NiCE CCaaS platform to stand up agents in a scalable manner.
Infrastructure as Code
Containerization and Orchestration
Data Lakes
Scripting Tools
CI/CD and Version Control
Testing Automation
Agile Development
Data Governance Principles

Interpersonal Skills
Analytical Thinking and Problem-Solving
Strong Communication Skills
Adaptability & Continuous Learning