Managed Services AI Platform Architect

AHEAD

4d•$225,000 - $250,000

About The Position

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation. At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD. We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived. We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD. The Managed Services AI Platform Architect is a hands-on technical leader responsible for designing, building, and scaling AI-powered operational capabilities across the Managed Services organization. This role is primarily execution-focused, with direct accountability for delivering agentic AI workflows that improve service performance and efficiency at scale. The architect will embed AI into core service delivery processes to drive measurable outcomes including incident deflection and ticket reduction, MTTR improvement, self-healing and automated remediation, knowledge-driven service delivery, and enhanced operational efficiency and client experience. While contributing to architecture standards and roadmap alignment, success in this role is measured by platform delivery, adoption, and operational impact.

Requirements

Proven hands-on experience designing and delivering AI/LLM-based systems (agentic AI, RAG, orchestration) and cloud-native platforms and integrations
Strong background in IT operations, managed services, or service delivery environments, with experience in automation and workflow optimization
Experience integrating with ITSM platforms (e.g., ServiceNow), observability and monitoring tools, and automation frameworks and scripting environments
Ability to translate operational challenges into AI-driven solutions with a strong execution mindset focused on delivering measurable outcomes

Nice To Haves

Experience building or deploying agentic AI systems in production environments
Familiarity with AIOps, self-healing systems, and intelligent automation
Experience working in multi-tenant or managed services delivery models
Exposure to enterprise AI platforms, governance, and scaling patterns

Responsibilities

Design, build, and evolve the Managed Services AI platform — delivering production-grade AI capabilities integrated directly into service delivery workflows
Lead the development of agentic AI solutions, including incident triage and classification, automated remediation and resolution, knowledge retrieval and summarization, and workflow orchestration and escalation
Drive use cases from concept through prototype to production, ensuring real operational adoption
Design and implement agent-based architectures including triage agents, resolution and remediation agents, RAG-based knowledge agents, and orchestration and multi-step workflow agents
Define patterns for prompt design and structured outputs, tool integration and action execution, memory and state management, and human-in-the-loop controls
Ensure AI workflows are observable, reliable, and continuously improving
Architect and integrate AI capabilities across core platforms including ITSM (ServiceNow), monitoring and observability tools, automation frameworks and runbooks, and knowledge management systems
Embed AI directly into day-to-day operational workflows — not standalone solutions — designed for multi-tenant, scalable managed services environments
Establish and maintain practical AI architecture standards and reusable patterns based on production usage
Contribute to the Managed Services AI roadmap, grounded in delivered capabilities and business impact
Define and enforce guardrails for safe automation: approval workflows and escalation paths, risk boundaries and controls, and observability and auditability
Align with enterprise architecture standards where appropriate, while prioritizing speed and execution
Drive measurable improvements across Managed Services operations: incident deflection rates, MTTR reduction, automation and self-healing coverage, ticket volume reduction, analyst and engineer productivity, and service quality and client experience
Partner closely with Managed Services delivery teams, automation and platform engineering teams, and operations leadership
Act as a player-coach — combining deep technical contribution with leadership and enablement
Drive adoption and scaling of AI capabilities across the organization