Data & AI ProductOps Lead

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data/AI, Information Systems, or a related field.
• Relevant certifications across cloud, Data & AI platforms, DevOps/MLOps, Service Management, architecture, or data management are an advantage.
• 10+ years of experience across Data Engineering, AI/ML, DataOps, DevOps, SRE, Product Operations, Platform Engineering or related disciplines.
• Strong hands-on experience with modern enterprise data platforms and production Data/AI solutions.
• Demonstrated experience establishing or leading production operating capabilities for Data and AI products.
• Strong practical experience across DataOps, MLOps, LLMOps and AgentOps.
• Strong experience with operational readiness, observability, SLAs/SLOs, monitoring, support models, incident and problem management, CI/CD, automated testing, release management, recovery and rollback.
• Experience operating BI and data products, data pipelines, APIs, integrations, ML models, GenAI applications and AI Agents in production.
• Practical experience with model lifecycle management, model serving, drift monitoring, RAG, vector search, AI evaluation, AI observability, Agent tracing, tool execution and guardrails.
• Strong understanding of enterprise ITSM and Service Management, with experience integrating Data and AI product teams into established support, incident, problem, change and release processes.
• Strong incident leadership, troubleshooting, root-cause analysis and production problem-solving capabilities.
• Demonstrated experience developing technical standards, operating models, reusable patterns and implementation methods.
• Strong client-facing consulting, architecture, solutioning and stakeholder-management capabilities.
• Strong experience working with government agencies or large enterprises in Qatar or the GCC is highly preferred.
• Data & Analytics Platforms: Databricks, Informatica IDMC, Microsoft Fabric, Power BI, Azure Data Services, Spark and Delta Lake.
• Data Integration & Streaming: Informatica, Kafka, APIs, batch integration, streaming and CDC patterns.
• AI, ML & GenAI: MLflow, Azure AI/ML, model serving and monitoring, RAG, vector search, AI evaluation and AI observability.
• GenAI & Agent Frameworks: LangChain, LangGraph, Semantic Kernel or equivalent GenAI and Agent orchestration frameworks.
• DevOps & Platform Engineering: Azure DevOps, GitHub, CI/CD, Docker, Kubernetes, Terraform, Python and SQL.
• Observability & Monitoring: OpenTelemetry, Azure Monitor, Application Insights or equivalent enterprise monitoring platforms.
• Service Management: ServiceNow or equivalent enterprise ITSM platforms.

Preferred Technology Experience

Practice Development

Product Operations

Client Delivery

Leadership Expectations

Responsibilities

• Establish and lead the Data & AI ProductOps practice, including the operating model, standards, methods, governance, reusable patterns, playbooks and implementation approach.
• Define the practice across DataOps, MLOps, LLMOps and AgentOps, with clear standards for operating Data, BI, ML, GenAI and Agent-based products in production.
• Develop reusable ProductOps assets, including operational readiness standards, support models, SLO frameworks, monitoring patterns, runbooks, implementation templates and delivery accelerators.
• Define and implement operational readiness requirements covering product ownership, criticality, support levels, SLAs/SLOs, monitoring, alerting, runbooks, escalation, recovery, dependencies and rollback.
• Establish clear L0/L1/L2/L3 support models, with L0 focused on automation and self-service, L1 on Service Desk support and initial triage, L2 on ProductOps-led operational support and product-level troubleshooting, and L3 on complex issues requiring Engineering expertise.
• Establish product health and observability across availability, performance, data freshness and quality, pipeline and integration health, model and AI performance, usage, cost and other product-specific operational measures.
• Lead product-level incident investigation, restoration and root-cause analysis, coordinating across Engineering, Platform, Service Management, Security and vendors.
• Integrate ProductOps with enterprise Incident, Problem, Request, Change, Release, Knowledge, Service Level and Major Incident Management processes.
• Ensure recurring incidents and operational issues are converted into permanent corrective actions, automation opportunities and product improvement backlog items.
• Establish effective change and release practices supported by automated testing, CI/CD, versioning, controlled deployment, and rollback.
• Establish regular Product Operations reviews covering product health, SLOs, incidents, recurring problems, operational trends, technical debt, improvements and releases.
• Assess clients Data & AI ProductOps maturity and define target operating models, support structures, service levels, observability, automation and implementation roadmaps.
• Design and implement ProductOps capabilities for Data, BI, ML, GenAI and Agent-based products within clients’ environments.
• Lead clients solutioning, architecture workshops and implementation engagements, defining roles, responsibilities, support models, operational processes, tooling and Service Management integration.
• Provide architecture and implementation oversight for complex Data and AI production environments, engaging senior stakeholders across Product, Engineering, Platform, Governance, Security and Service Management.
• Support proposals, technical solutioning and advisory engagements across Data & AI ProductOps, DataOps, MLOps, LLMOps and AgentOps.
• Build and scale a new Data & AI ProductOps practice.
• Provide strong technical leadership while remaining hands-on where required.
• Lead multidisciplinary teams across Data, AI, Engineering, Platform, Product and Service Management.
• Provide architecture and implementation oversight across complex enterprise environments.
• Engage confidently with senior clients and internal stakeholders.
• Translate emerging Data and AI technologies into practical enterprise operating practices.
• Develop internal talent and build sustainable technical capability.