Data & AI ProductOps Lead

Job Overview

This senior technical leadership position demands extensive practical expertise in modern data and AI infrastructure, encompassing DevOps/DataOps, production AI, observability, and integration with Service Management systems.

Key Performance Indicators

• Successful establishment, adoption, and scaling of the Data & AI ProductOps practice.
• Development of clear, reusable standards for DataOps, MLOps, LLMOps, and AgentOps.
• Implementation of ProductOps capabilities within client environments.
• Defined ownership, operational preparedness, and hierarchical L0-L3 support models.
• Enhanced product condition and compliance with Service Level Objectives (SLOs).
• Reduction in incident detection and recovery durations.
• Minimized recurring production problems and decreased unnecessary engineering escalations.
• Increased automation, proactive monitoring tools, and promotion of self-service.
• Robust integration between Data & AI ProductOps and Enterprise Service Management frameworks.
• Wider utilization of standardized patterns, playbooks, and delivery accelerators.
• Development of internal expertise and efficient client knowledge transfer.

Responsibilities

Practice Development:

• Lead and shape the Data & AI ProductOps practice, including operating models, standards, governance, reusable assets, playbooks, and implementation strategies.
• Define practice parameters across DataOps, MLOps, LLMOps, and AgentOps to operate Data, BI, ML, GenAI, and Agent-based products in production environments.
• Create reusable ProductOps resources such as operational readiness benchmarks, support frameworks, SLO guidelines, monitoring schemas, runbooks, templates, and accelerators.

Product Operations:

• Set and execute operational readiness criteria covering ownership, product criticality, support tiers, SLAs/SLOs, monitoring, alerts, escalation procedures, recovery methods, dependencies, and rollback strategies.
• Develop support models across L0 (automation and self-service), L1 (Service Desk and initial triage), L2 (ProductOps-led operational support), and L3 (complex engineering support).
• Monitor and manage product health and observability metrics such as availability, performance, data quality, pipeline integrity, model and AI performance, usage, and cost.
• Lead incident management including investigation, restoration, and root cause analysis, coordinating multiple teams and external vendors.
• Incorporate ProductOps within enterprise Incident, Problem, Change, Release, Knowledge, Service Level, and Major Incident Management processes.
• Convert repetitive incidents and operational challenges into permanent fixes, automation projects, and product enhancements.
• Institutionalize change and release management supported by automated testing, continuous integration/delivery, version control, staged deployments, and rollback plans.
• Conduct regular product operations reviews focused on health, SLO adherence, incident analysis, problem trends, technical debt, improvements, and releases.

Client Delivery:

• Evaluate client maturity in Data & AI ProductOps and develop targeted operating models, support structures, observability tools, automation, and implementation pathways.
• Design and deploy ProductOps solutions for Data, BI, ML, GenAI, and Agent products in client infrastructures.
• Lead client workshops on solution design, architecture, and execution, defining roles, responsibilities, support and operational processes, tooling, and Service Management integration.
• Oversee architecture and deployment studies for complex Data and AI environments, engaging senior stakeholders in Product, Engineering, Platform, Governance, Security, and Service Management.
• Assist in proposals, technical consulting, and advisory roles covering various ProductOps domains.

Leadership Expectations:

• Build and expand a new Data & AI ProductOps practice.
• Provide expert technical leadership while remaining actively engaged in hands-on tasks where necessary.
• Guide cross-functional teams across Data, AI, Engineering, Platform, Product, and Service Management domains.
• Deliver architecture and implementation governance on complex enterprise projects.
• Confidently engage senior clients and internal stakeholders.
• Translate emerging Data and AI technologies into practical, enterprise-ready operational standards.
• Develop internal talent and foster sustainable technical skills.

Qualifications and Experience

• Minimum bachelor’s degree in Computer Science, Engineering, Data/AI, Information Systems, or related disciplines.
• Certifications in cloud technologies, Data & AI platforms, DevOps/MLOps, Service Management, architecture, or data management are beneficial.
• At least 10 years of professional experience in Data Engineering, AI/ML, DataOps, DevOps, Site Reliability Engineering (SRE), Product Operations, Platform Engineering, or related fields.
• Extensive hands-on involvement with modern enterprise data platforms and Data/AI production solutions.
• Proven record in creating or leading production operating capabilities for Data and AI products.
• Advanced know-how in DataOps, MLOps, LLMOps, and AgentOps practices.
• Expertise in operational readiness, observability, SLA/SLO adherence, monitoring, support structures, incident and problem management, continuous integration/deployment, automated testing, release management, recovery, and rollback techniques.
• Experience managing BI and data products, pipelines, APIs, integrations, machine learning models, GenAI applications, and AI Agents in production settings.
• Practical knowledge of model lifecycle management, model deployment, drift detection, retrieval-augmented generation (RAG), vector search, AI evaluation, AI observability, agent tracing, tool execution, and operational guardrails.
• Strong familiarity with enterprise IT Service Management and integrating Data & AI teams within established support, incident, problem, change, and release workflows.
• Demonstrated skills in incident leadership, troubleshooting, root-cause analysis, and resolution of production issues.
• Experience developing technical standards, operational models, reusable frameworks, and deployment methodologies.
• Competence in client engagement, technical consulting, architectural design, solutioning, and stakeholder communication.
• Desirable experience working with government or large-scale enterprises in Qatar or the GCC region.

Preferred Technologies

• Data & Analytics Platforms: Databricks, Informatica IDMC, Microsoft Fabric, Power BI, Azure Data Services, Spark, Delta Lake.
• Data Integration & Streaming: Informatica, Kafka, APIs, batch processing, streaming, change data capture (CDC).
• AI, Machine Learning & GenAI Tools: MLflow, Azure AI/ML, model serving/monitoring, RAG, vector search, evaluation, observability.
• GenAI & Agent Frameworks: LangChain, LangGraph, Semantic Kernel or similar orchestration frameworks.
• DevOps & Platform Engineering Tools: Azure DevOps, GitHub, CI/CD pipelines, Docker, Kubernetes, Terraform, Python, SQL.
• Observability & Monitoring Platforms: OpenTelemetry, Azure Monitor, Application Insights, or equivalent tools.
• Service Management Systems: ServiceNow or comparable enterprise ITSM platforms.