Data & AI Managed Services Solution Architect, Manager

Job Summary

Data AI Managed Services Solution Architect, Manager - GMx Solutioning, Global Managed Services

Managed Services Solution Architect, Manager - Global Managed Services

As a Data AI Managed Services Solution Architect, you will shape and articulate differentiated managed services across data platforms and pipelines, data management, analytics and insights, and AI, ML and agent operations. Beyond designing the solution, you will ensure it is clearly understood, commercially compelling, operationally credible and aligned to responsible AI and data governance requirements.

You will work across global pursuit teams, collaborating with Sales, Delivery, Pricing, Data and AI leaders, platform specialists and alliance teams to convert complex DataOps, MLOps/LLMOps and Agent Ops capabilities into a cohesive, client-ready narrative.

Your key responsibilities

• Data AI solution strategy and narrative development
• Translate client priorities, CDO agenda, data and AI maturity, service issues and evaluation criteria into a clear solution storyline
• Develop value propositions that connect service design to business outcomes and a practical roadmap from stabilization to intelligent operations
• Convert complex DataOps, data governance, analytics, MLOps/LLMOps and Agent Ops constructs into simple executive messaging
• Ability to translate Data AI operating landscapes into clear, client-relevant managed services narratives and measurable outcomes

• End-to-end Data AI Managed Services solution design
• Strong understanding of the managed services lifecycle from pursuit and transition through stabilization, steady state and continuous improvement
• Design integrated services across Data Platform Pipeline Operations, Data Management Operations, Insights Operations, and AI, ML Agent Operations
• Ensure platform availability, pipeline resilience, observability, performance, capacity, FinOps, data quality, metadata, lineage
• Define scope, service catalogue, operating and delivery models, governance, coverage, tooling, interfaces and continuous improvement approach, optimization approach, SLAs
• Strong collaboration skills across global, multi-disciplinary pursuits and comfort operating in multi-owner environments

• Articulation and client engagement
• Actively participate in pursuits, contributing to proposals, solution documentation, executive presentations and orals
• Structure the solution around client context, design intent, service operating model, transition and stabilization, service maturity and value realization
• Lead or support discovery workshops, solution walkthroughs and client discussions across DataOps, Insights Ops, MLOps/LLMOps and Agent Ops
• Partner with Sales, account leadership and Data AI specialists to keep the client narrative, solution design and commercials aligned

• Transition, stabilization and production readiness
• Work with transition architect to ensure transition plans are practical, sequenced and aligned to data availability, client readiness, platform release cycles and regulatory obligations
• Strong understand of transition design principles and methodology

• Estimation and commercial input
• Translate service design into volumes, complexity drivers, coverage, role mix, staffing, location model, transition effort, productivity and automation assumptions
• Experience linking solution scope to demand drivers, staffing, locations, productivity, tooling and commercials
• Work with Pricing to align commercials with service tiers, retained responsibilities, tooling, cloud consumption, alliance dependencies and outcomes
• Highlight assumptions, exclusions, risks, contingencies and dependencies affecting commercial viability

10+ years of experience across Data AI, technology managed services, data platform operations, analytics application management, AI operations or adjacent production services

• Experience with major data and AI ecosystems such as Databricks, Snowflake, Microsoft Azure

• Experience with reusable assets or automation for monitoring, data quality, triage, remediation, deployment and operational productivity, observability, self-healing
• Familiarity with managed services pricing and cloud/platform cost drivers, including FinOps considerations
• Relevant architecture, cloud, data, AI, service management (e.g. ITIL) or FinOps certifications

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