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 Engineering

• Production model deployment

• CI/CD and automation

• Model registries and experiment tracking

• Kubernetes / OpenShift / Docker

• Cloud AI platforms

• Model monitoring and observability

Data / AI Architect Profile

Strong experience in areas such as:

• Enterprise or solution data architecture

• Data lakes, warehouses or lakehouses

• Data modelling and integration

• Data domains and ownership

• Data lineage and metadata

• Data governance and data quality

• AI and analytics data architectures

Technical 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 Management

• Data Governance

• Privacy and data protection

• AI governance / Responsible AI

• ISO/IEC 42001

• ISO/IEC 27001

• NIST AI RMF

• Banking AI or model-risk requirements

Required 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.