Managed Services AI Platform Architect

Key Responsibilities

AI Platform Delivery

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

Agentic AI & Workflow Architecture

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

Platform Integration & Operationalization

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

Standards, Guardrails & Roadmap

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

Operational Outcomes & Metrics

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

Execution Leadership

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

Required Qualifications

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

Preferred Qualifications

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

Why AHEAD:

USA Employment Benefits include:

Use of AI:

Originally posted on Himalayas