MLOps Engineer / Data Architect – AI & Data Platforms

MLOps Engineer / Data Architect – AI & Data Platforms-Qatar & UAE Role Overview Command Post is seeking an experienced MLOps Engineer / Data Architect to support the delivery of AI, data and assurance capabilities within a major banking environment. We are open to candidates from either of two backgrounds: · MLOps / AI Platform Engineering – focused on operationalising ML, GenAI and AI workloads, CI/CD, model lifecycle, deployment and monitoring. · Data Architecture / AI Data – focused on enterprise data architecture, data domains, lineage, integration, metadata and trusted data foundations for AI. Candidates do not need to be equally strong in both areas but should understand how AI, data, governance, security and compliance come together in a regulated environment. Key Responsibilities Design and implement MLOps processes covering model development, validation, deployment, monitoring, revalidation and retirement.Establish CI/CD, model versioning, experiment tracking and controlled promotion across DEV, TEST, UAT and PROD.Integrate AI/ML platforms such as MLflow, Databricks, Azure ML, SageMaker, Vertex AI or equivalent.Design enterprise data architectures supporting AI, analytics and business applications.Map data lineage across source systems, data platforms, datasets, models and production AI services.Define relationships between data assets, business departments, business processes and data domains.Support data quality, metadata, classification, privacy, retention, residency and access requirements.Support GenAI and agentic AI architectures including RAG, vector databases, model endpoints, prompts, tools and autonomous workflows.Contribute to AI assurance across governance, data, model, security, operations and regulatory compliance.Work closely with Data Science, Data Governance, Enterprise Architecture, Cybersecurity, Risk, Compliance and business teams. Candidate Profile MLOps / AI Platform Profile Strong experience in areas such as: MLOps or ML EngineeringProduction model deploymentCI/CD and automationModel registries and experiment trackingKubernetes / OpenShift / DockerCloud AI platformsModel monitoring and observabilityData / AI Architect Profile Strong experience in areas such as: Enterprise or solution data architectureData lakes, warehouses or lakehousesData modelling and integrationData domains and ownershipData lineage and metadataData governance and data qualityAI and analytics data architecturesTechnical Experience Experience with some of the following would be advantageous: MLOps / AI: MLflow, Databricks, Azure ML, SageMaker, Vertex AI, Kubeflow, GitHub/GitLab, Jenkins, Python, Kubernetes. Data: SQL/NoSQL, ETL/ELT, APIs, data catalogues, lineage, data quality, feature stores, vector databases and metadata platforms. AI / GenAI: LLMs, RAG, embeddings, AI agents, model evaluation, explainability, fairness, drift and AI security. Banking & Governance Experience Experience in banking, financial services or another regulated environment is strongly preferred. Knowledge of any of the following would be advantageous: Model Risk ManagementData GovernancePrivacy and data protectionAI governance / Responsible AIISO/IEC 42001ISO/IEC 27001NIST AI RMFBanking AI or model-risk requirementsRequired Experience 5+ years' relevant experience across MLOps, ML Engineering, Data Engineering, Data Architecture or related disciplines.Experience working in complex enterprise environments.Strong understanding of system and data integration.Ability to work across technical, architecture, governance and business teams.Strong design and documentation skills. What We Are Looking For We are looking for candidates who understand how AI and data move from concept into controlled enterprise production. The ideal candidate will operate across: AI + Data + Architecture + Engineering + Governance + Security + Risk and help customers accelerate the safe adoption of ML, GenAI and agentic AI within regulated environments.