AI Platform Engineer: MLOps & Production Reliability

Boundless; Dubai; On-site; AED 360,000 - 600,000; Full time

Job summary

Boundless in Dubai seeks a senior platform engineer to build and operate the shared infrastructure enabling AI/ML systems to move from development into production. You will ensure reproducible deployment, model/versioning, monitoring, security and cost visibility across generative AI services.

You will design environments for model training and online inference, implement CI/CD pipelines and model registries, while maintaining reliability and controlled access across platforms.

Qualifications

• 5+ years in DevOps, SRE, platform engineering or ML infrastructure.
• Experience with a major cloud platform, containers, infrastructure as code and CI/CD.
• Python or scripting skills and practical Linux, networking, identity and secrets-management knowledge.
• Experience with model serving, experiment tracking, artifact registries and reproducible deployment practices.
• Experience building observability, capacity planning, incident response and recovery processes for production services.
• Understanding of the operating characteristics of LLM APIs and model inference, including variable latency, usage limits and cost controls.
• Bachelor's degree in Computer Science, Engineering or a related field, or equivalent infrastructure engineering experience.

Responsibilities

• Design and maintain development, testing and production environments for model training, batch scoring, online inference and LLM-enabled applications.
• Build CI/CD pipelines with artifact versioning, automated checks, environment promotion, approvals and tested rollback for code, models and configuration.
• Implement model registries, experiment tracking and reproducible execution environments, linking deployed artifacts to their code, data and evaluation records.
• Operate model-serving endpoints, approved model gateways and container workloads; enable controlled access to third-party and internally hosted models.
• Monitor latency, throughput, availability, error rates, resource utilisation and model or token costs across AI services.
• Provide instrumentation for data drift and model quality signals in collaboration with model owners, separating infrastructure alerts from analytical performance alerts.
• Implement autoscaling, quotas, rate limits, timeouts, fallback behaviour and graceful degradation appropriate to each service’s operating requirements.
• Define service objectives, runbooks and recovery procedures; test backups, restoration and deployment rollback and participate in the agreed incident response rota.
• Apply infrastructure as code, least-privilege access, secrets management, network controls and approved data residency settings with Security and Infrastructure teams.
• Protect training data, model artifacts and logs through access restrictions, encryption and retention controls; prevent secrets or unnecessary client data from entering telemetry.
• Optimise CPU/GPU capacity, inference configurations and cloud spend, making application and model costs visible to technical and business owners.
• Review provider changes, dependency vulnerabilities and platform lifecycle risks; maintain platform documentation and reusable deployment patterns for engineering teams.

Skills

DevOps

SRE

Platform engineering

ML infrastructure

CI/CD

Python

Linux

Networking

Secrets management

Model serving

Experiment tracking

Observability

Capacity planning

Incident response

Cloud platforms

Education

Bachelor's degree in CS/Engineering

Tools

Kubernetes

MLflow

Model-serving frameworks

GPU workload optimisation

Job description

Boundless in Dubai seeks a senior platform engineer to build and operate the shared infrastructure enabling AI/ML systems to move from development into production. You will ensure reproducible deployment, model/versioning, monitoring, security and cost visibility across generative AI services.

You will design environments for model training and online inference, implement CI/CD pipelines and model registries, while maintaining reliability and controlled access across platforms.