MLOps Engineer / Data Architect – AI & Data Platforms

Role Overview

Command Post QFZ LLC is looking for a seasoned MLOps Engineer / Data Architect to enhance AI, data, and assurance capabilities in a significant banking environment based in Doha, Qatar. Applicants may come from two primary expertise areas: MLOps / AI Platform Engineering or Data Architecture / AI Data. The ideal candidate will understand the convergence of AI, data, governance, security, and compliance within regulated sectors.

Responsibilities

• Develop and implement end-to-end MLOps workflows including model development, validation, deployment, ongoing monitoring, revalidation, and decommissioning.
• Establish continuous integration and deployment (CI/CD) pipelines, manage model versioning, track experiments, and ensure controlled progression through development, testing, user acceptance, and production stages.
• Integrate AI/ML platforms such as MLflow, Databricks, Azure ML, SageMaker, Vertex AI, or equivalents.
• Design comprehensive enterprise data architectures that support AI, analytics, and business objectives.
• Trace and document data lineage from source systems through datasets, models, and deployed AI services.
• Define connections among data assets, business units, processes, and data domains.
• Ensure adherence to data quality, metadata management, classification protocols, privacy, retention policies, residency, and access controls.
• Support advanced AI architectures including Generative AI and agentic AI involving Retrieval-Augmented Generation (RAG), vector databases, model endpoints, prompt engineering, tools, and autonomous workflows.
• Contribute to AI assurance efforts including governance, data and model security, operational procedures, and regulatory compliance.
• Collaborate effectively with multidisciplinary teams including Data Science, Data Governance, Enterprise Architecture, Cybersecurity, Risk, Compliance, and business stakeholders.

Candidate Profile

Applicants may come from one of two backgrounds:

• MLOps / AI Platform Engineering: Experience with MLOps or machine learning engineering, deploying models into production, CI/CD automation, model registries, experiment tracking, container orchestration platforms like Kubernetes, OpenShift, or Docker, cloud AI services, and monitoring for model performance and observability.
• Data Architecture / AI Data: Expertise in enterprise or solution-level data architecture, data lakes and warehouses or lakehouses, data modeling and integration, managing data domains and ownership, lineage and metadata management, data governance, data quality improvement, and designing AI and analytics data frameworks.

Technical Skills

• Familiarity with tools such as MLflow, Databricks, Azure ML, SageMaker, Vertex AI, Kubeflow, GitHub/GitLab, Jenkins, Python, Kubernetes for MLOps and AI platform engineering.
• Proficiency in databases (SQL/NoSQL), ETL/ELT processes, APIs, data cataloging, feature stores, vector databases, and metadata management platforms.
• Experience with AI/GenAI technologies including large language models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, AI agents, model evaluation techniques, model explainability, fairness considerations, drift detection, and AI security measures.

Banking & Governance Experience

• Background in banking, financial services, or other highly regulated industries is strongly preferred.
• Knowledge of model risk management, data governance, privacy and data protection regulations, AI governance including Responsible AI frameworks, and relevant standards such as ISO/IEC 42001, ISO/IEC 27001, NIST AI RMF, and banking AI or model-risk compliance is advantageous.

Experience Required

• Minimum five years of relevant experience in MLOps, ML Engineering, Data Engineering, Data Architecture, or closely related fields.
• Proven experience operating within complex enterprise-scale environments.
• Strong grasp of system integration and data integration concepts and practices.
• Ability to collaborate effectively with diverse technical, architectural, governance, and business functions.
• Excellent skills in system design and thorough documentation.

Ideal Candidate Traits

The successful candidate will have a comprehensive understanding of the lifecycle by which AI and data projects transition from conceptual ideas to securely governed enterprise-level production environments. They will work at the intersection of AI, data, architecture, engineering, governance, security, and risk, supporting accelerated but safe adoption of machine learning, generative AI, and agentic AI technologies in regulated sectors.