Senior MLOps Engineer

Fortytwo

. Deploy scalable, production-ready ML services with optimized infrastructure and auto-scaling Kubernetes clusters .

Posted 11/11/2026full-timeRemote • United Arab EmiratesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying scalable ML services and optimizing infrastructure using Kubernetes and GPU resources. Proficient in CI/CD pipelines, model serving frameworks, and monitoring tools to ensure high availability and performance in MLOps environments.

Highest-signal resume keywords

KubernetesGPU OptimizationCI/CD PipelinesTriton Inference ServerMonitoring Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel OptimizationCloud PlatformsScripting LanguagesContainerization TechnologiesAdvanced ML TechniquesDistributed TrainingData EncryptionGo ProgrammingRust Programming

Tools & Technologies

GrafanaPrometheusHelmGitHub ActionsAirflowElasticsearchOpenSearchVLLMTGIONNX

Industry Keywords

MLOpsML EngineeringCloud StorageHigh AvailabilitySecure Model Deployment

Tech Stack

Tools & technologies

AirflowAWSAzureCloudElasticSearchGoogle Cloud PlatformGrafanaKubernetesNode.jsPrometheusPythonRustGo

About the role

Key responsibilities & impact

Deploy scalable, production-ready ML services with optimized infrastructure and auto-scaling Kubernetes clusters
Optimize GPU resources using MIG (Multi-Instance GPU) and NOS (Node Offloading System)
Manage cloud storage such as S3 for high availability and performance
Integrate LoRA and model merging workflows into ML processes
Work with and adapt state-of-the-art ML codebases to organizational needs
Deploy and manage LLMs, SLMs, and LMMs
Serve ML models using Triton Inference Server
Use vLLM, TGI, and other state-of-the-art serving frameworks
Optimize models with ONNX and TensorRT
Develop RAG systems integrating spreadsheet, math, and compiler processors
Set up monitoring and logging using Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch
Write and maintain CI/CD pipelines using GitHub Actions
Create Helm templates for rapid Kubernetes node deployment
Automate workflows using cron jobs and Airflow DAGs

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
Proficiency in Kubernetes, Helm, and containerization technologies
Experience with GPU optimization (MIG, NOS) and cloud platforms (AWS, GCP, Azure)
Strong knowledge of monitoring tools (Grafana, Prometheus) and scripting languages (Python, Bash)
Hands-on experience with CI/CD tools and workflow management systems
Familiarity with Triton Inference Server, ONNX, and TensorRT for model serving and optimization
Preferred: 5+ years of experience in MLOps or ML engineering roles
Preferred: Experience with advanced ML techniques, such as multi-sampling and dynamic temperatures
Preferred: Knowledge of distributed training and large model fine-tuning
Preferred: Proficiency in Go or Rust programming languages
Preferred: Experience designing and implementing highly secure MLOps pipelines, including secure model deployment and data encryption

Benefits

Comp & perks

Engage in meaningful AI research
Work on decentralized inference, multi-agent systems, and efficient model deployment
Build scalable and sustainable AI systems
Collaborate with a highly technical team
Autonomy in a research-driven environment
Opportunity to work on foundational AI challenges