MLOps Engineer

Salt; Abu Dhabi Emirate; On-site; AED 201,000 - 335,000; Full time

Job summary

Salt is seeking an experienced MLOps Engineer to design, build, and operate secure, scalable ML platforms on AWS. The role focuses on production-grade pipelines, automation, and governance, working with data scientists, engineers and security teams to ensure compliant, cost-efficient model lifecycle management.

You will implement CI/CD, IaC, and containerised deployments, optimise compute and storage, and drive observability, alerting and runbooks for reliable model delivery.

Qualifications

• At least 7+ years of relevant experience in MLOps, ML engineering, DevOps, cloud engineering or production software engineering.

Responsibilities

• Design and implement scalable MLOps architectures on AWS.

Skills

AWS SageMaker

Python

CI/CD

Terraform

Docker

Kubernetes

Dataiku integration

Model governance

Tools

Terraform

Docker

Kubernetes

AWS CloudFormation

SageMaker

Airflow

Job description

Salary - AED 18,000 - AED 30,000 Per Month

Contract Length - 1 Year (View to extend)

Start Date - ASAP

Job Purpose

To design, build and operate secure, scalable and governed Machine Learning Operations (MLOps) capabilities that enable reliable deployment and lifecycle management of AI/ML models. The role is responsible for production-grade ML pipelines, cloud infrastructure, automation, observability and operational controls, with a primary focus on AWS. It works closely with Data Scientists, Data Engineers, Platform Engineering, Information Security, Model Risk and business stakeholders to accelerate model delivery while maintaining resilience, traceability, cost efficiency and regulatory compliance

Accountabilities

• Design and implement scalable MLOps architectures, reusable deployment patterns and standard engineering components across development, testing and production environments, primarily on AWS
• Build and maintain automated workflows for data preparation, training, validation, model registration, approval, deployment and retraining using orchestration and pipeline capabilities such as Amazon SageMaker Pipelines, Step Functions, Airflow or equivalent tools
• Implement CI/CD for ML applications and infrastructure using Git-based workflows, automated testing, artifact management and Infrastructure as Code tools such as Terraform or AWS CloudFormation
• Deploy and operate batch and real‑time inference services using appropriate patterns, including managed endpoints, containers, Kubernetes or serverless services. Ensure versioning, rollback, scalability and high availability
• Implement monitoring for model quality, data drift, service health, latency, throughput, failures and infrastructure. Establish alerts, dashboards, incident response and operational runbooks
• Engineer solutions using relevant AWS services such as SageMaker, S3, ECR, ECS/EKS, Lambda, Step Functions, CloudWatch, IAM, KMS and Secrets Manager, applying secure networking and least‑privilege access controls
• Where applicable, configure and integrate Dataiku with enterprise data, AWS services and CI/CD processes. Support governed deployment, automation, monitoring and promotion of models across environments.
• Maintain model lineage, reproducibility, approvals, audit trails and technical documentation. Ensure adherence to data privacy, information security, Responsible AI, model risk and regulatory requirements
• Optimise compute, storage and inference configurations for performance, resilience and cost. Implement tagging, usage monitoring, capacity controls and practical FinOps measures
• Partner with Data Scientists, Data Engineers and application teams to remove productionisation blockers, improve engineering standards, review code and promote reusable MLOps practices
• Apply approved policies and procedures, continuously improve platform automation and support practices, and deliver responsive service to internal and external stakeholders.
• Demonstrate Our Promise and apply the ADCB Service Standards to deliver the Bank’s required levels of service in all internal and external customer interactions
• At least 7+ years of relevant experience in MLOps, ML engineering, DevOps, cloud engineering, platform engineering or production software engineering, including substantial hands‑on responsibility for deploying and operating ML solutions in production.
• Strong hands‑on AWS experience is required, particularly with Amazon SageMaker and associated services for storage, containers, orchestration, monitoring, identity and security.
• Proven experience building production‑grade ML pipelines, CI/CD workflows, Infrastructure as Code, containerised deployments, monitoring and operational support.
• Experience with Dataiku is strongly preferred, especially platform integration, automation, deployment, administration or governed model lifecycle management.
• Experience in banking, financial services or another regulated environment is preferred

Professional Experience

• AWS certification such as AWS Certified Machine Learning Engineer - Associate, AWS Certified DevOps Engineer - Professional or AWS Certified Solutions Architect is desirable.
• Dataiku certification is an advantage. Kubernetes, Terraform or relevant DevOps certifications are also desirable

Knowledge & Skills

• Advanced AWS and Amazon SageMaker engineering MLOps architecture and end-to‑end ML lifecycle management Python and shell scripting; working knowledge of SQL CI/CD using GitHub Actions, GitLab CI, Jenkins, AWS Code Pipeline or similar tools
• Infrastructure as Code using Terraform or AWS CloudFormation, Docker and Kubernetes, including Amazon ECR and EKS/ECS, MLflow, model registries, experiment tracking, and artifact management
• Batch and real‑time model deployment pattern Model, data and service monitoring; drift detection and observability
• Dataiku platform experience and integration with AWS Cloud security, IAM, encryption, secrets management and secure networking Model governance, lineage, reproducibility, auditability and documentation Troubleshooting, incident management, performance tuning and cost optimisation
• Strong collaboration, stakeholder communication and technical documentation skills